GNSS-Remote Sensing for Ionosphere Research

**Chapter 1**

**Abstract**

**1. Introduction**

**3**

GNSS High-Rate Data and the

The work discusses the efficiency of different ionospheric scintillation indices. The new index D2fi based on the GNSS carrier phase observable was introduced. We analyze the accuracy of the phase measurements**,** in particular its dependence on the GNSS equipment thermal noises, multipath and external noises, and

presettings of Phase Lock Loop and Code Delay Discriminator. The performance of DROTI, *S4*, *σφ*, and D2fi was considered for the case of high-rate data. The "sensitivity" and reliability of each index differs significantly and depends on the time resolution of the carrier phase data. The new index D2fi advantages are that it is easily derived and has a clear dependence on GNSS hardware and software features. D2fi was proven to be a useful tool to detect the small-scale ionospheric distur-

GNSS data with high-rate sampling becomes more and more available worldwide [1, 2]. It provides opportunities for the better results in the field of ionospheric scintillation research. Standard ionospheric indices and parameters *S4*, *σφ*, and *ROTI-*based indices and ionospheric total electron (TEC) are widely used for the ionospheric research as reliable and informative tools [3–6]. Unfortunately, their accuracy, efficiency, and reliability depend on the integration time, input data sampling rate, and de-trending and filtering procedures of the carrier phase time series [7–9]. GNSS hardware architecture, Code Delay Discriminator (CDD), and Phase Lock Loop (PLL) presets play the crucial role in the carrier phase measurement quality especially under multipath environment conditions [10, 11]. The mentioned issues bring uncertainties to the ionospheric indices calculations which, in turn, can degrade the experimental results interpretation. The sensitivity of the ionospheric indices/parameters depends on the time resolution of input data. One of the important questions is whether the data rate is high enough to be sure that all the necessary ionospheric information is derived. There are different works on GPS scintillation, for instance, [1, 7, 12], but still there is a room for the more profound analysis of the data of higher time resolution than 10 Hz. Such a high-rate data is often considered as a noise but it is not exactly the truth. The excellent results by [1]

Efficiency of Ionospheric

*Vladislav V. Demyanov, Maria A. Sergeeva*

bances based on high-rate GPS carrier phase measurements.

**Keywords:** GPS, ionospheric scintillation indices, high-rate GNSS data

Scintillation Indices

*and Anna S. Yasyukevich*

#### **Chapter 1**

## GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices

*Vladislav V. Demyanov, Maria A. Sergeeva and Anna S. Yasyukevich*

#### **Abstract**

The work discusses the efficiency of different ionospheric scintillation indices. The new index D2fi based on the GNSS carrier phase observable was introduced. We analyze the accuracy of the phase measurements**,** in particular its dependence on the GNSS equipment thermal noises, multipath and external noises, and presettings of Phase Lock Loop and Code Delay Discriminator. The performance of DROTI, *S4*, *σφ*, and D2fi was considered for the case of high-rate data. The "sensitivity" and reliability of each index differs significantly and depends on the time resolution of the carrier phase data. The new index D2fi advantages are that it is easily derived and has a clear dependence on GNSS hardware and software features. D2fi was proven to be a useful tool to detect the small-scale ionospheric disturbances based on high-rate GPS carrier phase measurements.

**Keywords:** GPS, ionospheric scintillation indices, high-rate GNSS data

#### **1. Introduction**

GNSS data with high-rate sampling becomes more and more available worldwide [1, 2]. It provides opportunities for the better results in the field of ionospheric scintillation research. Standard ionospheric indices and parameters *S4*, *σφ*, and *ROTI-*based indices and ionospheric total electron (TEC) are widely used for the ionospheric research as reliable and informative tools [3–6]. Unfortunately, their accuracy, efficiency, and reliability depend on the integration time, input data sampling rate, and de-trending and filtering procedures of the carrier phase time series [7–9]. GNSS hardware architecture, Code Delay Discriminator (CDD), and Phase Lock Loop (PLL) presets play the crucial role in the carrier phase measurement quality especially under multipath environment conditions [10, 11]. The mentioned issues bring uncertainties to the ionospheric indices calculations which, in turn, can degrade the experimental results interpretation. The sensitivity of the ionospheric indices/parameters depends on the time resolution of input data. One of the important questions is whether the data rate is high enough to be sure that all the necessary ionospheric information is derived. There are different works on GPS scintillation, for instance, [1, 7, 12], but still there is a room for the more profound analysis of the data of higher time resolution than 10 Hz. Such a high-rate data is often considered as a noise but it is not exactly the truth. The excellent results by [1] based on the amplitude and phase measurements with the data rate of 100 Hz demonstrated the new opportunity to look at, and far beyond, 10 Hz resolution. The ionospheric scintillations show different features at different GNSS frequencies. Hence, the single-frequency carrier phase measurements can be involved for more informative analysis. The final accuracy of the carrier phase measurements depends on the GNSS equipment internal noises, multipath, and external noises. Incorrect presettings of PLL and CDD as well as the bad quality of reference oscillator can mislead a researcher in his or her final conclusions. To mitigate the impact of the mentioned factors, it is necessary to preset the receiver hardware (including antenna, preamplifier, and inter-frequency filter) and software (PLL and CDD types and parameters).

Let us estimate the values of the main components of the carrier phase noise. They should be small enough to obtain the pure ionospheric phase scintillation based on the D2fi index. In case of the stationary receiver, there are no phase variations and phase measurement noises due to vibration and jerks. Based on this assumption, the noise error in carrier phase measurements depends on two main factors**:** the carrier-tonoise ratio at the PLL input and the multipath noises at the reception point.

For an ideal PLL without inner loss, the noise dispersion of phase measurements

ð Þ <sup>2</sup>*<sup>π</sup>* <sup>2</sup> <sup>∙</sup> *<sup>Δ</sup>FPLL* 2 ∙*CN*<sup>0</sup>

where *ΔFPLL* is the noise bandwidth of the PLL filter (Hz) and *CN*<sup>0</sup> is the

Thus, the noise level of the carrier phase measurements is determined by the carrier-to-noise ratio (CNR) at the PLL input. The CNR depends on (1) the level of external noises, (2) the antenna pattern, and (3) the low-noise preamplifier (LPA) gain. In addition to external noise, the inherit receiver thermal noise, the short-term instability of the reference oscillator, the signal sampling, and quantization noise

According to expression (1)**,** the final accuracy of the carrier phase measurements depends on the filter noise bandwidth. At the same time, the carrier-to-noise ratio at the PLL input depends on the time of accumulation of instantaneous phase measurement samples. Thus, the noise dispersion of phase measurements can be

*σφ* <sup>¼</sup> <sup>1</sup>

*<sup>T</sup>* is the dispersion of receiver thermal noise and *σ*<sup>2</sup>

The noise components of the phase measurements with dispersions *σ*<sup>2</sup>

*ΔFPLL CN*<sup>0</sup>

*σ<sup>F</sup>* ¼ *m* ∙

samples (ms), *σF*ð Þ*τ* is the RMS of the short-term instability of the reference generator frequency (Hz), *f* is the signal carrier frequency, and *m* is a coefficient depending on the PLL filter type (*m* = 144 for a second-order PLL filter and *m* = 160

ð Þ <sup>2</sup>*<sup>π</sup>* <sup>2</sup> <sup>∙</sup>

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *σ*2 *<sup>T</sup>* <sup>þ</sup> *<sup>σ</sup>*<sup>2</sup> *F*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1 2*TCOR* ∙*CN*<sup>0</sup> <sup>s</sup> � � (3)

∙ 1 þ

where *TCOR* is the time of accumulation of instantaneous phase measurement

The carrier-to-noise ratio at the PLL input is a function of the receiver noise temperature (including the antenna), as well as the environment noise temperature (the Earth noise, the total noise of cosmic radio sources, and the Sun noise). The measurements of noise caused by analog-to-digital signal conversion, as well as signal-to-noise level with regard to filtering, amplification, and antenna gain, can be expressed through the corresponding loss in the resulting carrier-to-noise ratio at the phase detector input. Therefore, the carrier-to-noise ratio at the PLL input can

*σF*ð Þ*τ* ∙*f ΔFPLL*

q

(1)

(2)

(4)

*<sup>T</sup>* and *σ*<sup>2</sup> *F*

*<sup>F</sup>* is the dispersion of

*σ*2 *<sup>φ</sup>* <sup>¼</sup> <sup>1</sup>

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

carrier-to-noise ratio at the PLL unit input (dBW).

is determined as follows [17]:

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

should be considered as well.

where *σ*<sup>2</sup>

determined more precisely as follows [17]:

noise caused by the Allan deviation.

for a third-order PLL filter).

be expressed as follows [17]:

**5**

depend on the above factors as follows [17]:

*<sup>σ</sup><sup>T</sup>* <sup>¼</sup> <sup>1</sup> <sup>2</sup>*<sup>π</sup>* <sup>∙</sup>

It is important to find such an ionospheric scintillation index which is easily derived and has a clear dependence on both the ionospheric turbulence structure and GNSS hardware and software presets. In this work, the second-order derivative of the GPS signal carrier phase based on high-rate carrier phase time series is suggested as a promising means for the ionospheric scintillation detection. No additional complex processing is needed to obtain this new scintillation index.

The work [1] and the general necessity to define the GPS data time resolution sufficient for the robust scintillation analysis were the motivation for the authors to test the real sensitivity of the ionospheric indices depending on the input data sampling rate. We consider GNSS carrier phase observable to be the most capable of observing the ionospheric disturbances and scintillations. The aims of this study include (a) introduction of the new index that is the second-order derivative of the GPS signal carrier phase (D2fi index) which helps to reveal scintillation events; (b) test of sensitivity of D2fi, DROTI, S4, and σφ indices based on 50 Hz GPS data; and (c) consideration of the benefits and limitations of these indices for scintillation studies. The analysis was performed for the case study and was based on GPS data of the mid-latitude GNSS station located near Irkutsk, Russia, during the intense geomagnetic storm.

#### **2. The carrier phase noise content at the phase lock loop input**

Ionospheric phase scintillations are induced with ionospheric irregularities of hundreds of meters to several kilometer size. These irregularities correspond to the Fresnel frequencies from ≈ 0.1 to ≈ 10 Hz [13, 14]. According to [1, 2, 15]**,** it is possible to detect small-scale ionospheric irregularities of hundreds of meters to several kilometer size by observing not only the fast carrier phase variations but also the carrier phase noise variations which were considered earlier as "noise" [1]. This is possible if the data sampling rate is high enough to exclude low-frequency variations and trends from the carrier phase time series. The data sampling rate should contain the sufficient ionospheric information. The authors [1] showed that the majority of the phase scintillation events can be revealed if data sampling rate between 10 and 40 Hz is used. Therefore, for the analysis of weak ionospheric scintillations, the sampling data rate higher than 10 Hz should be used.

To extract the phase noise variations from the complex carrier phase data, we use the carrier phase derivatives. The second-order derivative works as a high-pass filter and removes the phase ambiguity, all the low-frequency trends (due to the relative motion between satellite and receivers), multipath slow variations, and low-frequency phase variations due to reference oscillator frequency drift. It allows us to extract the phase noise variations from the phase measurements without additional complex processing procedures. The carrier phase noise derivative can be also used as a new parameter in GPS occultation technology [16].

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

Let us estimate the values of the main components of the carrier phase noise. They should be small enough to obtain the pure ionospheric phase scintillation based on the D2fi index. In case of the stationary receiver, there are no phase variations and phase measurement noises due to vibration and jerks. Based on this assumption, the noise error in carrier phase measurements depends on two main factors**:** the carrier-tonoise ratio at the PLL input and the multipath noises at the reception point.

For an ideal PLL without inner loss, the noise dispersion of phase measurements is determined as follows [17]:

$$
\sigma\_{\varphi}^{2} = \frac{1}{\left(2\pi\right)^{2}} \bullet \frac{\Delta F\_{PLL}}{2 \bullet CN\_{0}} \tag{1}
$$

where *ΔFPLL* is the noise bandwidth of the PLL filter (Hz) and *CN*<sup>0</sup> is the carrier-to-noise ratio at the PLL unit input (dBW).

Thus, the noise level of the carrier phase measurements is determined by the carrier-to-noise ratio (CNR) at the PLL input. The CNR depends on (1) the level of external noises, (2) the antenna pattern, and (3) the low-noise preamplifier (LPA) gain. In addition to external noise, the inherit receiver thermal noise, the short-term instability of the reference oscillator, the signal sampling, and quantization noise should be considered as well.

According to expression (1)**,** the final accuracy of the carrier phase measurements depends on the filter noise bandwidth. At the same time, the carrier-to-noise ratio at the PLL input depends on the time of accumulation of instantaneous phase measurement samples. Thus, the noise dispersion of phase measurements can be determined more precisely as follows [17]:

$$
\sigma\_{\varphi} = \frac{1}{\left(2\pi\right)^{2}} \bullet \sqrt{\sigma\_{T}^{2} + \sigma\_{F}^{2}} \tag{2}
$$

where *σ*<sup>2</sup> *<sup>T</sup>* is the dispersion of receiver thermal noise and *σ*<sup>2</sup> *<sup>F</sup>* is the dispersion of noise caused by the Allan deviation.

The noise components of the phase measurements with dispersions *σ*<sup>2</sup> *<sup>T</sup>* and *σ*<sup>2</sup> *F* depend on the above factors as follows [17]:

$$
\sigma\_T = \frac{1}{2\pi} \bullet \sqrt{\frac{\Delta F\_{PLL}}{\text{CN}\_0} \bullet \left(1 + \frac{1}{2T\_{COR} \bullet \text{CN}\_0}\right)}\tag{3}
$$

$$
\sigma\_F = m \bullet \frac{\sigma\_F(\tau) \bullet f}{\Delta F\_{PLL}} \tag{4}
$$

where *TCOR* is the time of accumulation of instantaneous phase measurement samples (ms), *σF*ð Þ*τ* is the RMS of the short-term instability of the reference generator frequency (Hz), *f* is the signal carrier frequency, and *m* is a coefficient depending on the PLL filter type (*m* = 144 for a second-order PLL filter and *m* = 160 for a third-order PLL filter).

The carrier-to-noise ratio at the PLL input is a function of the receiver noise temperature (including the antenna), as well as the environment noise temperature (the Earth noise, the total noise of cosmic radio sources, and the Sun noise). The measurements of noise caused by analog-to-digital signal conversion, as well as signal-to-noise level with regard to filtering, amplification, and antenna gain, can be expressed through the corresponding loss in the resulting carrier-to-noise ratio at the phase detector input. Therefore, the carrier-to-noise ratio at the PLL input can be expressed as follows [17]:

based on the amplitude and phase measurements with the data rate of 100 Hz demonstrated the new opportunity to look at, and far beyond, 10 Hz resolution. The ionospheric scintillations show different features at different GNSS frequencies. Hence, the single-frequency carrier phase measurements can be involved for more informative analysis. The final accuracy of the carrier phase measurements depends on the GNSS equipment internal noises, multipath, and external noises. Incorrect presettings of PLL and CDD as well as the bad quality of reference oscillator can mislead a researcher in his or her final conclusions. To mitigate the impact of the mentioned factors, it is necessary to preset the receiver hardware (including antenna, preamplifier, and inter-frequency filter) and software (PLL and CDD

It is important to find such an ionospheric scintillation index which is easily derived and has a clear dependence on both the ionospheric turbulence structure and GNSS hardware and software presets. In this work, the second-order derivative of the GPS signal carrier phase based on high-rate carrier phase time series is suggested as a promising means for the ionospheric scintillation detection. No additional complex processing is needed to obtain this new scintillation index. The work [1] and the general necessity to define the GPS data time resolution sufficient for the robust scintillation analysis were the motivation for the authors to test the real sensitivity of the ionospheric indices depending on the input data sampling rate. We consider GNSS carrier phase observable to be the most capable of observing the ionospheric disturbances and scintillations. The aims of this study include (a) introduction of the new index that is the second-order derivative of the GPS signal carrier phase (D2fi index) which helps to reveal scintillation events; (b) test of sensitivity of D2fi, DROTI, S4, and σφ indices based on 50 Hz GPS data; and (c) consideration of the benefits and limitations of these indices for scintillation studies. The analysis was performed for the case study and was based on GPS data of the mid-latitude GNSS station located near Irkutsk, Russia, during the intense

**2. The carrier phase noise content at the phase lock loop input**

Ionospheric phase scintillations are induced with ionospheric irregularities of hundreds of meters to several kilometer size. These irregularities correspond to the Fresnel frequencies from ≈ 0.1 to ≈ 10 Hz [13, 14]. According to [1, 2, 15]**,** it is possible to detect small-scale ionospheric irregularities of hundreds of meters to several kilometer size by observing not only the fast carrier phase variations but also the carrier phase noise variations which were considered earlier as "noise" [1]. This is possible if the data sampling rate is high enough to exclude low-frequency variations and trends from the carrier phase time series. The data sampling rate should contain the sufficient ionospheric information. The authors [1] showed that the majority of the phase scintillation events can be revealed if data sampling rate between 10 and 40 Hz is used. Therefore, for the analysis of weak ionospheric scintillations, the sampling data rate higher than 10 Hz should be used.

To extract the phase noise variations from the complex carrier phase data, we use the carrier phase derivatives. The second-order derivative works as a high-pass filter and removes the phase ambiguity, all the low-frequency trends (due to the relative motion between satellite and receivers), multipath slow variations, and low-frequency phase variations due to reference oscillator frequency drift. It allows us to extract the phase noise variations from the phase measurements without additional complex processing procedures. The carrier phase noise derivative can be

also used as a new parameter in GPS occultation technology [16].

types and parameters).

*Satellites Missions and Technologies for Geosciences*

geomagnetic storm.

**4**

*Satellites Missions and Technologies for Geosciences*

$$\text{CNV}\_0 = P\_{\text{rcc}} + \text{G}\_A - \text{N}\_T - \text{L}\_{\text{tr}} - \text{L}\_{\text{dg}} \tag{5}$$

The sky noise temperature (*ТCN*), including all cosmic radio noise sources, can be considered equal to 100 K [18]. This noise is accepted for the entire antenna aperture. If we consider an ideal antenna without losses, the corresponding sky

The inherit antenna noise temperature TA results from the noise of active loss

where *T0* is the antenna physical temperature and *η* is the antenna efficiency. If the antenna temperature is equal to 300 K and the typical antenna efficiency is

where ε is the preamplifier noise coefficient and *T01* is the receiver physical temperature. Let us assume that the typical noise coefficient for the modern preamplifiers varies from 1.4 to 2 dB and *T01* is 300 K. These conditions result in *TLNA* ≈ 120 ... 300 K, and the corresponding thermal noise power is from �204 to

**Table 1** shows the values of noise temperatures and noise spectral power for the

Using the information from the **Table 1** and formulas (2)–(5), we can estimate the noise level of the phase measurements in a stationary receiver when measuring the phase at different GPS frequencies and satellite elevations. Let us assume that *ΔFPLL* ¼ 18*Hz*, accumulation time *TCOR* ¼ 20*mc*, Allan deviation of the reference generator *<sup>σ</sup>F*ð Þ¼ *<sup>τ</sup>* <sup>10</sup>�11, the maximum and minimum power levels of the signals (*Prec*), received at L1, L2 and L5 frequencies are described by curves in **Figure 1** [19, 20]. The values of the standard deviation of the phase noise for this case are

The quality of the receiver radio-frequency chain (RFC) and the regular variations in the signal level at the reception point play an important role in the potential accuracy of the signal phase measurements. In particular, the sustainable phase tracking threshold equals 15° [17] is almost reached under conditions of the worst radio-frequency chain parameters (**Table 2**) and the minimum signal receiving

**Noise source Noise temperature, K Power spectral density dBW/Hz**

Preamplifier noise 120 … 300 �204 … �208 Antenna noise 30 … 60 �214 … �211 Sky noise (all sources) 100 �208 Earth noise 1.2 … 3 �228 … �224 Sun noise 0.06 �241 Total value 251.26 … 463.06 �204.6 … �202.1

above mentioned components of the receiver thermal noise (*NT*) and external noises. According to the table, the thermal noise at the PLL input significantly depends on the receiver hardware and the antenna pattern. This can result in the significantly different carrier phase measurements accuracy and noise level when

using navigation receivers and antennas of various types and models.

between 80 and 90%, the temperature *ТА* ≈ 60 … 30 K and the corresponding

The noise temperature of the preamplifier is defined as follows [18]:

*TA* ¼ *T*<sup>0</sup> ∙ð Þ 1 � *η* (9)

*TLPA* ¼ *T*<sup>01</sup> ∙ð Þ *ε* � 1 (10)

noise power at the PLL input is about �208 dBW/Hz.

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

antenna noise power is between �211 and � 214 dBW/Hz.

resistance in the antenna [18]:

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

�208 dBW/Hz.

given in **Table 2**.

**Table 1.**

**7**

*The receiver and external thermal noises.*

where *Prec* is the signal level at the receiving point (dBW); *GA* is the antenna gain (dB); *NT* is the spectral density of the receiver thermal noise power (dBW); *Ltr* is the total power loss during filtering, frequency conversion, and the signal attenuation in the cable (dB); and *Ldg* is the signal power loss due to its analog-to-digital conversion (dB).

According to formula (5), two factors affecting the carrier-to-noise ratio at the PLL input сan be deduced. The first factor is constant during the measurement and depends on the receiver equipment type. It is defined by the *Ltr, Ldg*, and *GA* values. These typical values are *Ltr =* � *2 …* �*4* dB, *Ldg* = � 0.55 … �3.0 dB, and *GA* = �2 … �7.5 dB (depending on the satellite line-of-site angular direction) [18].

At the same time, there is a factor that depends not only on the equipment type but also changes randomly. This is the receiver thermal noise *NT*. Let us estimate its change limits and reveal the most significant causes that affect the magnitude of this noise. The spectral power of the thermal noise is related to the temperature of medium [18]:

$$N\_T = \mathbf{10} \bullet \lg(K \bullet T\_{\Sigma}) \tag{6}$$

where *K* = 1.38∙10�<sup>23</sup> (W∙s/K) is the Boltzmann constant and *T<sup>Σ</sup>* is the total noise temperature of the equipment and the external environment, forming measurement noise.

The total noise temperature can be estimated as follows [18]:

$$T\_{\Sigma} = T\_{\text{EXN}} + T\_A + T\_{\text{LPA}} \tag{7}$$

where *ТEXN* is external noise due to the Earth noise (*ТEN*), the noise of the Galaxy and cosmic radio sources (*TCN*), and the Sun noise (*ТSN*); *TA* is the antenna noise temperature caused by the active loss resistance noise in the antenna; and *ТLPA* is the noise temperature of a low-noise preamplifier.

Under standard physical conditions, the Earth noise temperature is *TΣ*,*EN* ¼ 300*K*. The Earth noise component, which is present at the PLL input (*ТΣ,EN*), is determined by the antenna pattern as follows [18]:

$$T\_{EN} = 100 \bullet \left(\frac{\beta}{2\theta}\right)^2 \bullet T\_{\Sigma,EN} \tag{8}$$

where *<sup>β</sup>* <sup>2</sup>*<sup>θ</sup>* is the ratio of the angular aperture of a groundward part of the antenna pattern, with respect to the total angular aperture of the antenna pattern.

According to Eq. (8)**,** the higher the *<sup>β</sup>* <sup>2</sup>*<sup>θ</sup>* ratio**,** the higher the magnitude of the Earth noise. With regard to the known antenna pattern of typical navigation receiver antennas**,** the value *<sup>β</sup>* 2*θ* <sup>2</sup> can be within 0.004–0.01 [18]. Thus**,** the Earth noise temperature at the PLL input is *ТEN* = 1.2–3 *К*, and the correspondent noise spectral power varies from �227.8 to �223.8 dBW/Hz.

Similarly, the Sun noise temperature can be obtained. The total noise temperature of the Sun is *T<sup>Σ</sup>*,*<sup>S</sup>* ¼ 6000*K***.** The angular size of the Sun visible from the Earth's surface is βС = 0,5°. Considering the above mentioned typical antenna pattern**,** the *β* 2*θ* <sup>2</sup> ratio is about 10�<sup>5</sup> **.** When the sunlight falls into the antenna aperture, the Sun noise temperature ТSN = 0,00001 � 6000 ≈ 0,06 К. This corresponds to the Sun noise temperature at the PLL input of about -241 dBW/Hz.

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

The sky noise temperature (*ТCN*), including all cosmic radio noise sources, can be considered equal to 100 K [18]. This noise is accepted for the entire antenna aperture. If we consider an ideal antenna without losses, the corresponding sky noise power at the PLL input is about �208 dBW/Hz.

The inherit antenna noise temperature TA results from the noise of active loss resistance in the antenna [18]:

$$T\_A = T\_\bullet \bullet (\mathbf{1} - \eta) \tag{9}$$

where *T0* is the antenna physical temperature and *η* is the antenna efficiency.

If the antenna temperature is equal to 300 K and the typical antenna efficiency is between 80 and 90%, the temperature *ТА* ≈ 60 … 30 K and the corresponding antenna noise power is between �211 and � 214 dBW/Hz.

The noise temperature of the preamplifier is defined as follows [18]:

$$T\_{LPA} = T\_{01} \bullet (\varepsilon - \mathbf{1}) \tag{10}$$

where ε is the preamplifier noise coefficient and *T01* is the receiver physical temperature. Let us assume that the typical noise coefficient for the modern preamplifiers varies from 1.4 to 2 dB and *T01* is 300 K. These conditions result in *TLNA* ≈ 120 ... 300 K, and the corresponding thermal noise power is from �204 to �208 dBW/Hz.

**Table 1** shows the values of noise temperatures and noise spectral power for the above mentioned components of the receiver thermal noise (*NT*) and external noises. According to the table, the thermal noise at the PLL input significantly depends on the receiver hardware and the antenna pattern. This can result in the significantly different carrier phase measurements accuracy and noise level when using navigation receivers and antennas of various types and models.

Using the information from the **Table 1** and formulas (2)–(5), we can estimate the noise level of the phase measurements in a stationary receiver when measuring the phase at different GPS frequencies and satellite elevations. Let us assume that *ΔFPLL* ¼ 18*Hz*, accumulation time *TCOR* ¼ 20*mc*, Allan deviation of the reference generator *<sup>σ</sup>F*ð Þ¼ *<sup>τ</sup>* <sup>10</sup>�11, the maximum and minimum power levels of the signals (*Prec*), received at L1, L2 and L5 frequencies are described by curves in **Figure 1** [19, 20]. The values of the standard deviation of the phase noise for this case are given in **Table 2**.

The quality of the receiver radio-frequency chain (RFC) and the regular variations in the signal level at the reception point play an important role in the potential accuracy of the signal phase measurements. In particular, the sustainable phase tracking threshold equals 15° [17] is almost reached under conditions of the worst radio-frequency chain parameters (**Table 2**) and the minimum signal receiving


#### **Table 1.** *The receiver and external thermal noises.*

*CN*<sup>0</sup> ¼ *Prec* þ *GA* � *NT* � *Ltr* � *Ldg* (5)

*NT* ¼ 10 ∙*lg K*ð Þ ∙ *T<sup>Σ</sup>* (6)

*T<sup>Σ</sup>* ¼ *TEXN* þ *TA* þ *TLPA* (7)

∙ *T<sup>Σ</sup>*,*EN* (8)

<sup>2</sup>*<sup>θ</sup>* ratio**,** the higher the magnitude of the

can be within 0.004–0.01 [18]. Thus**,** the Earth

**.** When the sunlight falls into the antenna aperture, the Sun

where *Prec* is the signal level at the receiving point (dBW); *GA* is the antenna gain (dB); *NT* is the spectral density of the receiver thermal noise power (dBW); *Ltr* is the total power loss during filtering, frequency conversion, and the signal attenuation in the cable (dB); and *Ldg* is the signal power loss due to its analog-to-digital

According to formula (5), two factors affecting the carrier-to-noise ratio at the PLL input сan be deduced. The first factor is constant during the measurement and depends on the receiver equipment type. It is defined by the *Ltr, Ldg*, and *GA* values. These typical values are *Ltr =* � *2 …* �*4* dB, *Ldg* = � 0.55 … �3.0 dB,

At the same time, there is a factor that depends not only on the equipment type but also changes randomly. This is the receiver thermal noise *NT*. Let us estimate its change limits and reveal the most significant causes that affect the magnitude of this noise. The spectral power of the thermal noise is related to the temperature of

where *K* = 1.38∙10�<sup>23</sup> (W∙s/K) is the Boltzmann constant and *T<sup>Σ</sup>* is the total noise temperature of the equipment and the external environment, forming

where *ТEXN* is external noise due to the Earth noise (*ТEN*), the noise of the Galaxy and cosmic radio sources (*TCN*), and the Sun noise (*ТSN*); *TA* is the antenna noise temperature caused by the active loss resistance noise in the antenna; and

Under standard physical conditions, the Earth noise temperature is *TΣ*,*EN* ¼ 300*K*. The Earth noise component, which is present at the PLL input (*ТΣ,EN*), is

> 2*θ* <sup>2</sup>

<sup>2</sup>*<sup>θ</sup>* is the ratio of the angular aperture of a groundward part of the antenna

*TEN* <sup>¼</sup> <sup>100</sup> <sup>∙</sup> *<sup>β</sup>*

pattern, with respect to the total angular aperture of the antenna pattern.

2*θ* <sup>2</sup>

noise temperature at the PLL input of about -241 dBW/Hz.

spectral power varies from �227.8 to �223.8 dBW/Hz.

Earth noise. With regard to the known antenna pattern of typical navigation

noise temperature at the PLL input is *ТEN* = 1.2–3 *К*, and the correspondent noise

noise temperature ТSN = 0,00001 � 6000 ≈ 0,06 К. This corresponds to the Sun

Similarly, the Sun noise temperature can be obtained. The total noise temperature of the Sun is *T<sup>Σ</sup>*,*<sup>S</sup>* ¼ 6000*K***.** The angular size of the Sun visible from the Earth's surface is βС = 0,5°. Considering the above mentioned typical antenna pattern**,** the

and *GA* = �2 … �7.5 dB (depending on the satellite line-of-site angular

The total noise temperature can be estimated as follows [18]:

*ТLPA* is the noise temperature of a low-noise preamplifier.

determined by the antenna pattern as follows [18]:

According to Eq. (8)**,** the higher the *<sup>β</sup>*

receiver antennas**,** the value *<sup>β</sup>*

ratio is about 10�<sup>5</sup>

conversion (dB).

*Satellites Missions and Technologies for Geosciences*

direction) [18].

medium [18]:

measurement noise.

where *<sup>β</sup>*

*β* 2*θ* <sup>2</sup>

**6**

duration of the navigation message character is 20 ms [21], the accumulation of

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

of a quasi-optimal phase discriminator [21]. Therefore, the accumulation time should be within the interval 1*ms*≤*TCOR* ≤20*ms*. Here, the ranging code sequence length (1 ms for the CA code) determines the lower limit of the accumulation time (1 ms). The final decision about the optimal *TCOR* value is limited by two factors: (1) the carrier-to-noise ratio in the phase measuring channel and (2) the influence of the low-frequency processes on the phase measurement accuracy. The longer the time interval, the higher both the carrier-to-noise ratio and the phase measurement accuracy. However, with an increase of the accumulation time more than 10–20 ms, the effects of instability in the reference oscillator frequency and Doppler frequency drift can appear [21]. Therefore, it is not appropriate to increase the accumulation

After the determination of the optimal *TCOR* value, the selected PLL noise bandwidth should satisfy Eq. (11). In addition, according to Eq. (1) the noise level of the phase measurement depends on the noise bandwidth *ΔFPLL*. Therefore, the practical choice of the noise bandwidth depends on the expected measurement conditions and usually lies within the range from 10 to 20 Hz. If there is an impact of external electromagnetic interference, the phase tracking stability reduces. Therefore, the choice of the wider noise bandwidth increases the reliability of the phase tracking. Finally, according to expressions (3) and (4), the increase of the noise bandwidth leads to the proportional increase of the average RMS of the receiver equipment thermal noise. On the other hand, as *ΔFPLL* increases, the noise component related to the short-term frequency instability of the reference oscillator decreases. Thus, the noise bandwidth can be reduced without the significant loss of the phase measurement quality by using a better-quality reference oscillator.

The multipath effect is another important source of the carrier phase noises. In general, the phase error due to multipath can be calculated as a difference between the carrier phase of the reflected composite signal and the carrier phase of the direct signal. In the presence of multipath propagation, the composite signal phase shifts randomly from the direct signal phase, and the NCO-generated local carrier locks to the composite carrier phase, resulting in the error of the phase measurement. In the case of one reflected signal, the error of the phase measurement is defined as

*<sup>C</sup>* � *τ*<sup>1</sup> ∙ *sin φ*<sup>1</sup>

*<sup>C</sup>* � *τ*<sup>1</sup> ∙ *cos φ*<sup>1</sup> (12)

, and *φ*<sup>1</sup> is the phase of the reflected

*<sup>C</sup>* � *τ*<sup>1</sup> is the

*<sup>C</sup>* value computation is

*<sup>C</sup> α*<sup>1</sup> ∙ *R*ð Þ *τ*^

*<sup>C</sup>* is the autocorrelation function of the PRN code, *R*ð Þ *τ*^

1

*<sup>C</sup>* ) depends on the front-end bandwidth of the GNSS receiver

If the direct signal has no distortion in the form the PRN code**,** the autocorrela-

radio-frequency chain. The PRN codes have one main lobe and several side lobes in the frequency domain. In practice, the signal is band limited, and only the main lobe and one or more side lobes are used for the signal processing. As a result, the sharp correlation peaks are rounded and the ends are trailed-off. It was found earlier that the RFC bandwidth affects the maximum error value significantly [10, 11]. In the

cross-correlation function between the direct GNSS signal and the reflected signal,

*<sup>C</sup>* is the receiver estimate of the incoming signal code delay, *τ*<sup>1</sup> is the reflected signal code delay, *α*<sup>1</sup> is the reflection coefficient that corresponds to the Signal to

*<sup>Δ</sup><sup>Ψ</sup>* <sup>¼</sup> *arctan <sup>α</sup>*<sup>1</sup> <sup>∙</sup> *<sup>R</sup>*ð Þ *<sup>τ</sup>*^

*R*ð Þþ *τ*^

The measured parameter is not obligatory constant within *TCOR* interval in case

measurements should be *TCOR* ≥ *20* ms.

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

time over these limits.

follows [10]:

*τ*^

**9**

signal.

where *R*ð Þ *τ*^

tion function (*R*ð Þ *τ*^

Multipath Ratio (SMR) as *SMR* <sup>¼</sup> <sup>20</sup> <sup>∙</sup> *log <sup>α</sup>*�<sup>1</sup>

case of the unlimited bandwidth**,** the misalignment in the *τ*^

#### **Figure 1.**

*The power levels of signals received by a linearly polarized antenna with 3dBi antenna gain at L1, L2, and L5 GPS frequencies (the curves were reconstructed based on [19, 20]).*


#### **Table 2.**

*Noise values of phase measurements.*

level at the L2 frequency. Thus, although the phase measurements yield the best accuracy for ionospheric scintillation detection, still the careful presetting of GNSS receiver hardware and the consideration of measurement conditions are needed. To note, under the similar conditions, the best accuracy of the phase measurements is achieved if the signals are used at the L5 GPS frequency. This can be explained by the highest carrier-to-noise ratio in the given measurement channel (**Figure 1**).

Another important factor for the high accuracy of carrier phase measurements is the correct choice of the PLL settings such as accumulation time (*TCOR*) and the PLL filter noise bandwidth (*ΔFPLL*). It is known that the third-order tracking system has stable and unstable operation zones. If there are no impacts on the navigation receiver in the form of vibrations**,** jerks, and electromagnetic jammer interference, then the stable tracking of the carrier phase is provided with the following conditions fulfilled [17]:

$$0 < \Delta F\_{PLL} < \frac{0.7}{T\_{COR}} \tag{11}$$

When using an optimal phase discriminator, the measured parameter (phase) should not be changed during the accumulation time (*TCOR*). In this case, when estimating the phase, it is necessary to head for the longest character of the transmitted message. This is the character of the navigation message, which is transmitted simultaneously with the ranging code on the same carrier frequency. If the

#### *GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

duration of the navigation message character is 20 ms [21], the accumulation of measurements should be *TCOR* ≥ *20* ms.

The measured parameter is not obligatory constant within *TCOR* interval in case of a quasi-optimal phase discriminator [21]. Therefore, the accumulation time should be within the interval 1*ms*≤*TCOR* ≤20*ms*. Here, the ranging code sequence length (1 ms for the CA code) determines the lower limit of the accumulation time (1 ms). The final decision about the optimal *TCOR* value is limited by two factors: (1) the carrier-to-noise ratio in the phase measuring channel and (2) the influence of the low-frequency processes on the phase measurement accuracy. The longer the time interval, the higher both the carrier-to-noise ratio and the phase measurement accuracy. However, with an increase of the accumulation time more than 10–20 ms, the effects of instability in the reference oscillator frequency and Doppler frequency drift can appear [21]. Therefore, it is not appropriate to increase the accumulation time over these limits.

After the determination of the optimal *TCOR* value, the selected PLL noise bandwidth should satisfy Eq. (11). In addition, according to Eq. (1) the noise level of the phase measurement depends on the noise bandwidth *ΔFPLL*. Therefore, the practical choice of the noise bandwidth depends on the expected measurement conditions and usually lies within the range from 10 to 20 Hz. If there is an impact of external electromagnetic interference, the phase tracking stability reduces. Therefore, the choice of the wider noise bandwidth increases the reliability of the phase tracking. Finally, according to expressions (3) and (4), the increase of the noise bandwidth leads to the proportional increase of the average RMS of the receiver equipment thermal noise. On the other hand, as *ΔFPLL* increases, the noise component related to the short-term frequency instability of the reference oscillator decreases. Thus, the noise bandwidth can be reduced without the significant loss of the phase measurement quality by using a better-quality reference oscillator.

The multipath effect is another important source of the carrier phase noises. In general, the phase error due to multipath can be calculated as a difference between the carrier phase of the reflected composite signal and the carrier phase of the direct signal. In the presence of multipath propagation, the composite signal phase shifts randomly from the direct signal phase, and the NCO-generated local carrier locks to the composite carrier phase, resulting in the error of the phase measurement. In the case of one reflected signal, the error of the phase measurement is defined as follows [10]:

$$\Delta\Psi = \arctan\left(\frac{a\_1 \bullet R(\tau\_C^\circ - \tau\_1) \bullet \sin \varphi\_1}{R(\tau\_C^\circ) + a\_1 \bullet R(\tau\_C^\circ - \tau\_1) \bullet \cos \varphi\_1}\right) \tag{12}$$

where *R*ð Þ *τ*^ *<sup>C</sup>* is the autocorrelation function of the PRN code, *R*ð Þ *τ*^ *<sup>C</sup>* � *τ*<sup>1</sup> is the cross-correlation function between the direct GNSS signal and the reflected signal, *τ*^ *<sup>C</sup>* is the receiver estimate of the incoming signal code delay, *τ*<sup>1</sup> is the reflected signal code delay, *α*<sup>1</sup> is the reflection coefficient that corresponds to the Signal to Multipath Ratio (SMR) as *SMR* <sup>¼</sup> <sup>20</sup> <sup>∙</sup> *log <sup>α</sup>*�<sup>1</sup> 1 , and *φ*<sup>1</sup> is the phase of the reflected signal.

If the direct signal has no distortion in the form the PRN code**,** the autocorrelation function (*R*ð Þ *τ*^ *<sup>C</sup>* ) depends on the front-end bandwidth of the GNSS receiver radio-frequency chain. The PRN codes have one main lobe and several side lobes in the frequency domain. In practice, the signal is band limited, and only the main lobe and one or more side lobes are used for the signal processing. As a result, the sharp correlation peaks are rounded and the ends are trailed-off. It was found earlier that the RFC bandwidth affects the maximum error value significantly [10, 11]. In the case of the unlimited bandwidth**,** the misalignment in the *τ*^ *<sup>C</sup>* value computation is

level at the L2 frequency. Thus, although the phase measurements yield the best accuracy for ionospheric scintillation detection, still the careful presetting of GNSS receiver hardware and the consideration of measurement conditions are needed. To note, under the similar conditions, the best accuracy of the phase measurements is achieved if the signals are used at the L5 GPS frequency. This can be explained by the highest carrier-to-noise ratio in the given measurement channel (**Figure 1**). Another important factor for the high accuracy of carrier phase measurements is the correct choice of the PLL settings such as accumulation time (*TCOR*) and the PLL filter noise bandwidth (*ΔFPLL*). It is known that the third-order tracking system has stable and unstable operation zones. If there are no impacts on the navigation receiver in the form of vibrations**,** jerks, and electromagnetic jammer interference, then the stable tracking of the carrier phase is provided with the following condi-

*The power levels of signals received by a linearly polarized antenna with 3dBi antenna gain at L1, L2, and L5*

**Frequency, MHz Minimal value** *σφ***, deg Maximal value** *σφ***, deg**

L1 = 1575.42 1.59 7.22 L2 = 1227.60 3.35 14.85 L5 = 1176.45 1.33 6.06

*GPS frequencies (the curves were reconstructed based on [19, 20]).*

*Satellites Missions and Technologies for Geosciences*

0< *ΔFPLL* <

When using an optimal phase discriminator, the measured parameter (phase) should not be changed during the accumulation time (*TCOR*). In this case, when estimating the phase, it is necessary to head for the longest character of the transmitted message. This is the character of the navigation message, which is transmitted simultaneously with the ranging code on the same carrier frequency. If the

0*:*7 *TCOR*

(11)

tions fulfilled [17]:

**8**

**Figure 1.**

**Table 2.**

*Noise values of phase measurements.*

zero. In the case of 10 MHz bandwidth, the misalignment is not equal to zero and can vary within �0.03 *tC*, where *tC* is PRN code chip length. The narrower bandwidth of 2 MHz brings the significant misalignment to the calculation of the *τ*^ *C* value which can reach the values of � (0.1 … 0.3) *tC* [10].

The cross-correlation function *R*ð Þ *τ*^ *<sup>C</sup>* � *τ*<sup>1</sup> significantly depends on the early-late correlator spacing (*d*) and PRN code rate. It is well known that the code delay discriminator output (*Δd*,*out*) depends on the correlator spacing time (*Td*), the input tracking error ð Þ *τ*^ *<sup>C</sup>* � *τ<sup>c</sup>* , and the PRN code chip length (*tC*) as follows [17]:

$$\Delta\_{\rm d,out} = -2 \bullet \frac{(\tau\_{\rm C} - \tau\_{\rm c})}{\mathbf{t}\_{\rm C}}\_{\mathbf{t}\_{\rm eS} \,\rm Td} \tag{13}$$

than 1.5 chip delayed, it can cause the minor peak or a non-zero correlation value as well [24]. This effect is not significant for our analysis, thus we will not consider it

*The code multipath error in relation to the relative multipath delay at the fixed SMR = 3 dB and different*

The maximum error values of the phase measurement due to multipath are calculated according to Eq. (12). It was supposed that there is only one reflected signal that has the phase shift angle φ1,max, rad and delayed *τ*<sup>1</sup> seconds. This angle corresponds to the case when the multipath errors reach the maxima and affects the multipath error envelope which contains all the smaller variations of the ΔΨ values. The angle φ1,max can be found by differentiating Eq. (12) with respect to φ1, putting

*<sup>φ</sup>*1, *max* <sup>¼</sup> *cos* �<sup>1</sup> �*α*<sup>1</sup> <sup>∙</sup> *<sup>R</sup>*ð Þ *<sup>τ</sup>*^

**Figure 2** shows the standard deviations of the carrier phase multipath errors with respect to multipath delays for different SMR using the correlator spacing of *Td* = �0.1*tC* and the coherent discriminator for code tracking. The results in **Figure 2** correspond to L1 C/A PRN code (solid lines) and L5 I5 (Q5) PRN codes (dashed lines). Here**,** the unlimited RFC bandwidth and *τ<sup>1</sup>* variations within the range of � *tc* are supposed. The results are obtained changing the reflected signal relative phase shift by discrete steps of 0.1 of a total phase cycle, calculating the

*The multipath error envelopes for L1 C/a (solid lines) and L5 I5 (Q5) PRN codes (dashed lines) in relation to*

*<sup>C</sup>* � *τ*<sup>1</sup>

(15)

**Maximal relative multipath delay (***τ***<sup>1</sup> ∙** *c***), meters**

*R*ð Þ *τ*^ *C*

**Td =** �**0.5***tC* **Td =** �**0.1***tC* **Td =** �**0.5***tC* **Td =** �**0.1***tC*

it to zero and solving it for φ1. It results in the following [10]:

**Frequency/PRN code Maximal code multipath error**

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

**(***τ*^

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

*<sup>C</sup>* **∙** *c***), meters**

L1/CA 39.0 8.0 350 300 L5/I5, L5/Q5 4.0 0.8 40 35

further.

**Figure 2.**

*SMR.*

**11**

**Table 4.**

*correlator spacing.*

This equation describes the discriminator output in case if the input tracking error (τe) is within linear part of the discriminator performance. The maximal discriminator output value is limited by the correlator spacing time and depends on the code chip length [17]

$$\Delta\_{\rm d,MAX} = 2 \bullet \frac{\rm T\_{d}}{\rm tc\_{\rm \tiny \tiny \rm \tiny \rm \atop \rm T\_{\rm \tiny \rm \atop \rm T}}} \tag{14}$$

Thus, both the correlator spacing and the PRN code chip length define the maximal code tracking error value and, as a result, the cross-correlation function *R*ð Þ *τ*^ *<sup>C</sup>* � *τ*<sup>1</sup> . Let us consider the particular example of L1 C/A code and the coherent discriminator using a standard correlator with the correlator spacing of *Td* = �0.5*tC* and a narrow correlator with the spacing of *Td* = �0.1*tC*. The work [10] proved that for this particular case, the maximum and the minimum errors are much higher than the narrow correlator with 0.1 chip spacing. If the correlator spacing is �0.5*tC***,** the code tracking error lies within �0.4 … 0.5 *tC*. It corresponds to the code delay computation error *τ*^ *<sup>C</sup>* = � 120 … 150 m for C/A PRN code. In contrast to that**,** in the case of the narrow correlator**,** the code tracking error is *τ*^ *<sup>C</sup>* = � 25 … 30 m for C/A code.

To estimate the possible impact of the PRN code rate on the multipath error, the multipath error envelopes can be used [22, 23]. **Table 3** illustrates the GPS PRN code characteristics transmitted at L1 and L5 GPS frequencies. **Table 4** was reconstructed based on the results [23]**.** It illustrates the maximal code multipath error (*τ*^ *<sup>C</sup>* ∙*c*) in relation to the PRN code rate and correlator spacing for the coherent discriminator.

**Table 4** shows that the size of the area, where the multipath effect is significant, depends on the code rate or, to be exact, on the PRN chip length (*tc*). This is because the discriminator function value varies within 0 < *Td* < *tc*. If the input code tracking error exceeds *tc*, the discriminator function values saturate. Hence, the code discriminator is sensitive within � *tc*. Moreover, in the case of the standard correlator, the multipath error beyond the multipath delay of 1.5 *tc* can differ from zero. This is due to the fact that the PRN code autocorrelation characteristics has one major peak and many minor peaks [17]. If the reflected signal is received more


**Table 3.**

*L1, L2, and L5 GPS signal characteristics [19, 20].*


**Table 4.**

zero. In the case of 10 MHz bandwidth, the misalignment is not equal to zero and can vary within �0.03 *tC*, where *tC* is PRN code chip length. The narrower bandwidth of 2 MHz brings the significant misalignment to the calculation of the *τ*^

correlator spacing (*d*) and PRN code rate. It is well known that the code delay discriminator output (*Δd*,*out*) depends on the correlator spacing time (*Td*), the input

*Δ*d,out ¼ �2 ∙

*<sup>C</sup>* � *τ<sup>c</sup>* , and the PRN code chip length (*tC*) as follows [17]:

ð Þ τ^ <sup>C</sup> � τ<sup>c</sup> tC <sup>τ</sup>e≤Td

> Td tC <sup>τ</sup>e¼Td

*<sup>C</sup>* = � 120 … 150 m for C/A PRN code. In contrast to that**,** in the

This equation describes the discriminator output in case if the input tracking error (τe) is within linear part of the discriminator performance. The maximal discriminator output value is limited by the correlator spacing time and depends on

*Δ*d,MAX ¼ 2 ∙

Thus, both the correlator spacing and the PRN code chip length define the maximal code tracking error value and, as a result, the cross-correlation function

*<sup>C</sup>* � *τ*<sup>1</sup> . Let us consider the particular example of L1 C/A code and the coherent discriminator using a standard correlator with the correlator spacing of *Td* = �0.5*tC* and a narrow correlator with the spacing of *Td* = �0.1*tC*. The work [10] proved that for this particular case, the maximum and the minimum errors are much higher than the narrow correlator with 0.1 chip spacing. If the correlator spacing is �0.5*tC***,** the code tracking error lies within �0.4 … 0.5 *tC*. It corresponds to the code delay

To estimate the possible impact of the PRN code rate on the multipath error, the multipath error envelopes can be used [22, 23]. **Table 3** illustrates the GPS PRN code characteristics transmitted at L1 and L5 GPS frequencies. **Table 4** was reconstructed based on the results [23]**.** It illustrates the maximal code multipath

*<sup>C</sup>* ∙*c*) in relation to the PRN code rate and correlator spacing for the coherent

**Table 4** shows that the size of the area, where the multipath effect is significant, depends on the code rate or, to be exact, on the PRN chip length (*tc*). This is because the discriminator function value varies within 0 < *Td* < *tc*. If the input code tracking error exceeds *tc*, the discriminator function values saturate. Hence, the code discriminator is sensitive within � *tc*. Moreover, in the case of the standard correlator, the multipath error beyond the multipath delay of 1.5 *tc* can differ from zero. This is due to the fact that the PRN code autocorrelation characteristics has one major peak and many minor peaks [17]. If the reflected signal is received more

**Frequency/PRN code Carrier frequency, MHz Code rate (Mbps)** L1 C/A 1575.25 1.023 L5 I5, L5 Q5 1176.45 10.23

value which can reach the values of � (0.1 … 0.3) *tC* [10].

case of the narrow correlator**,** the code tracking error is *τ*^

The cross-correlation function *R*ð Þ *τ*^

*Satellites Missions and Technologies for Geosciences*

tracking error ð Þ *τ*^

the code chip length [17]

*R*ð Þ *τ*^

code.

error (*τ*^

**Table 3.**

**10**

*L1, L2, and L5 GPS signal characteristics [19, 20].*

discriminator.

computation error *τ*^

*C*

(13)

(14)

*<sup>C</sup>* = � 25 … 30 m for C/A

*<sup>C</sup>* � *τ*<sup>1</sup> significantly depends on the early-late

*The code multipath error in relation to the relative multipath delay at the fixed SMR = 3 dB and different correlator spacing.*

than 1.5 chip delayed, it can cause the minor peak or a non-zero correlation value as well [24]. This effect is not significant for our analysis, thus we will not consider it further.

The maximum error values of the phase measurement due to multipath are calculated according to Eq. (12). It was supposed that there is only one reflected signal that has the phase shift angle φ1,max, rad and delayed *τ*<sup>1</sup> seconds. This angle corresponds to the case when the multipath errors reach the maxima and affects the multipath error envelope which contains all the smaller variations of the ΔΨ values. The angle φ1,max can be found by differentiating Eq. (12) with respect to φ1, putting it to zero and solving it for φ1. It results in the following [10]:

$$\rho\_{1,\max} = \cos^{-1}\left(\frac{-a\_1 \bullet R(\tau\_C^\circ - \tau\_1)}{R(\tau\_C^\circ)}\right) \tag{15}$$

**Figure 2** shows the standard deviations of the carrier phase multipath errors with respect to multipath delays for different SMR using the correlator spacing of *Td* = �0.1*tC* and the coherent discriminator for code tracking. The results in **Figure 2** correspond to L1 C/A PRN code (solid lines) and L5 I5 (Q5) PRN codes (dashed lines). Here**,** the unlimited RFC bandwidth and *τ<sup>1</sup>* variations within the range of � *tc* are supposed. The results are obtained changing the reflected signal relative phase shift by discrete steps of 0.1 of a total phase cycle, calculating the

**Figure 2.**

*The multipath error envelopes for L1 C/a (solid lines) and L5 I5 (Q5) PRN codes (dashed lines) in relation to SMR.*

multipath error (ΔΨ) at each step and then taking their mean values and standard deviation.

The storm period was chosen for the analysis as geomagnetic storms are known to cause ionospheric disturbances including the small-scale disturbances that are of the particular interest for this work. The intense storm of June 22–25, 2015, was under analysis. **Figure 3** shows SYM-H index variations during the storm. Main phase (MP) and recovery phase (RP) of the storm are marked with red lines [27]. SYM-H reached its minimum on June 23rd. SYM-H index data was obtained from Data Analysis Center for Geomagnetism and Space Magnetism following the link http://wdc.kugi.kyoto-u.ac.jp/aeasy/index.html (last access: August 2018).

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

According to [3], the relationship between *S4*, *σφ*, and *DROTI* is complex, but in most cases the *S4* increase means *DROTI* increase and viсe versa. **Figure 4** shows variations of the D2fi index, *DROTI*, *S4*, and *σφ* indices during the storm for the GPS satellites PRN 04, PRN 15, and PRN 27 observed at ISTP station. Good

*Time behavior of D2fi and standard scintillation indices. The dots indicate the approximate SV angular positions when the scintillation events were observed. (a) Elevation of the satellites, (b) D2fi, (c) σφ,*

**Figure 4.**

**13**

*(d) S4 and (e) DROTI.*

According to **Figure 2,** there is a dependence of the error on SMR. The magnitude of the multipath error (ΔΨ) is proportional to the strength of the multipath signal. Moreover, the multipath error value is independent on the carrier wavelength (Eq. 12), but it is mostly a function of the antenna-reflector distance through the correlation function *R*ð Þ *τ*^ *<sup>C</sup>* � *τ*<sup>1</sup> . If the multipath delay (*τ1*) is high, the correlation value decreases and so does the multipath error amplitude. The maximal multipath error in the phase measurement does not exceed 0.6 rad under the above mentioned assumptions (**Figure 2**). However, under real conditions the multipath error is formed as a sum of several reflected signals or as a result of another kind of multipath sources such as diffuse scattering or diffraction. Thus, the higher values of the error of the phase measurement due to multipath can be expected. The authors [10, 11] demonstrated that the maximal value of the error due to multipath does not depend significantly on the code correlator spacing and there is no similar dependence on the code discriminator type as well.

#### **3. Experimental results and analysis**

#### **3.1 Indices comparison**

This section discusses the performance of the "standard" ionospheric scintillation indices and the index D2fi based on high-rate sampling data. The D2fi index and the ionospheric indices/parameters *TEC*, *DROTI, S4*, and *σφ* were compared during the geomagnetic storm conditions. The ionospheric scintillations are considered to be more typical for high and low latitudes. Mid-latitude scintillations are supposed to occur much less often. Here, first, we analyze the data of the midlatitude station where the scintillation detection is a rather challenging problem and estimate the indices performance. Then we consider the example of high latitudes.

The 50 Hz *L1* and *L2* GPS data were obtained at the mid-latitude station ISTP (Irkutsk, Russia, geographic coordinates 52° N, 104° E) equipped with JAVAD GNSS receiver. The station is a part of SibNet network [25, 26].

As the de-trended TEC data is used to calculate *DROTI* indices**,** the uncalibrated code-leveled phase TEC time series were derived from GPS phase and code measurements for this study. The phase TEC time series were de-trended by the centered moving window with 30 second accumulation time. *DROTI* values were calculated from the de-trended 50 Hz TEC data with 1 second time resolution based on [5]. The indices *S4* and *σφ* were calculated from the de-trended 50 Hz *L1* data based on the standard procedure [6] with 1 second time resolution as well.

**Figure 3.** *SYM-H variations during June 20–25, 2015. The MP and RP are indicated with the vertical red lines.*

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

The storm period was chosen for the analysis as geomagnetic storms are known to cause ionospheric disturbances including the small-scale disturbances that are of the particular interest for this work. The intense storm of June 22–25, 2015, was under analysis. **Figure 3** shows SYM-H index variations during the storm. Main phase (MP) and recovery phase (RP) of the storm are marked with red lines [27]. SYM-H reached its minimum on June 23rd. SYM-H index data was obtained from Data Analysis Center for Geomagnetism and Space Magnetism following the link http://wdc.kugi.kyoto-u.ac.jp/aeasy/index.html (last access: August 2018).

According to [3], the relationship between *S4*, *σφ*, and *DROTI* is complex, but in most cases the *S4* increase means *DROTI* increase and viсe versa. **Figure 4** shows variations of the D2fi index, *DROTI*, *S4*, and *σφ* indices during the storm for the GPS satellites PRN 04, PRN 15, and PRN 27 observed at ISTP station. Good

#### **Figure 4.**

*Time behavior of D2fi and standard scintillation indices. The dots indicate the approximate SV angular positions when the scintillation events were observed. (a) Elevation of the satellites, (b) D2fi, (c) σφ, (d) S4 and (e) DROTI.*

**13**

multipath error (ΔΨ) at each step and then taking their mean values and standard

tion value decreases and so does the multipath error amplitude. The maximal multipath error in the phase measurement does not exceed 0.6 rad under the above mentioned assumptions (**Figure 2**). However, under real conditions the multipath error is formed as a sum of several reflected signals or as a result of another kind of multipath sources such as diffuse scattering or diffraction. Thus, the higher values of the error of the phase measurement due to multipath can be expected. The authors [10, 11] demonstrated that the maximal value of the error due to multipath does not depend significantly on the code correlator spacing and there is no similar

According to **Figure 2,** there is a dependence of the error on SMR. The magnitude of the multipath error (ΔΨ) is proportional to the strength of the multipath signal. Moreover, the multipath error value is independent on the carrier wavelength (Eq. 12), but it is mostly a function of the antenna-reflector distance through

This section discusses the performance of the "standard" ionospheric scintillation indices and the index D2fi based on high-rate sampling data. The D2fi index and the ionospheric indices/parameters *TEC*, *DROTI, S4*, and *σφ* were compared during the geomagnetic storm conditions. The ionospheric scintillations are considered to be more typical for high and low latitudes. Mid-latitude scintillations are supposed to occur much less often. Here, first, we analyze the data of the midlatitude station where the scintillation detection is a rather challenging problem and estimate the indices performance. Then we consider the example of high latitudes. The 50 Hz *L1* and *L2* GPS data were obtained at the mid-latitude station ISTP (Irkutsk, Russia, geographic coordinates 52° N, 104° E) equipped with JAVAD

As the de-trended TEC data is used to calculate *DROTI* indices**,** the uncalibrated

code-leveled phase TEC time series were derived from GPS phase and code measurements for this study. The phase TEC time series were de-trended by the centered moving window with 30 second accumulation time. *DROTI* values were calculated from the de-trended 50 Hz TEC data with 1 second time resolution based on [5]. The indices *S4* and *σφ* were calculated from the de-trended 50 Hz *L1* data based on the standard procedure [6] with 1 second time resolution as well.

*SYM-H variations during June 20–25, 2015. The MP and RP are indicated with the vertical red lines.*

*<sup>C</sup>* � *τ*<sup>1</sup> . If the multipath delay (*τ1*) is high, the correla-

deviation.

the correlation function *R*ð Þ *τ*^

*Satellites Missions and Technologies for Geosciences*

dependence on the code discriminator type as well.

GNSS receiver. The station is a part of SibNet network [25, 26].

**3. Experimental results and analysis**

**3.1 Indices comparison**

**Figure 3.**

**12**

correlation between the D2fi index and *σφ* variations is seen for all the scintillation events and for all considered satellites, including the weakest event for PRN 04 (**Figure 4b** and **c**). Nevertheless, the peaks of the D2fi index are pronounced more sharply for all the cases. The correlation between the D2fi index and *S4* variations is worse. There is a general similarity in behavior of these parameters, but *S4* distribution is rather noisy and contains several peaks which do not coincide in time with the peaks of the D2fi index (**Figure 4b** and **d**).

The worst correlation is between the D2fi index and *DROTI* for all the cases under consideration (**Figure 4b** and **e**). The form of *DROTI* envelope significantly differs from the envelope of the D2fi index. To add, *DROTI* observations are rather noisy. Almost no *DROTI* response is seen for the SV PRN 27 (**Figure 4e**, middle panel). The small-scale ionospheric irregularities do not provoke significant TEC response [28]. Consequently, even weaker response can be expected in TEC-derived indices like *DROTI*, which is probably the case of **Figure 4e**.

Let us consider the advantages of the D2fi index in comparison to other indices by the example in **Figure 4**. First, it marks the sharper and more precise in time response to small-scale turbulences than other indices. Furthermore, only one GPS frequency is needed to obtain the D2fi index. Thus, it avoids the possible impact from the inter-frequency noises and *L1*-aiding technique features [23]**.** Third, as the D2fi index is calculated from either *L1* or *L2* phase data, it does not require any additional preprocessing and does not depend on the data processing technique [8]. Finally, another advantage of the D2fi index is its high sensitivity. We recall that mid-latitudes are usually considered as the region where the scintillation occurrence is null except during geomagnetic disturbances. Even for the presented case, the scintillation intensity is very low (S4 is not higher than 0.1, **Figure 4d**). Nevertheless, the D2fi index response on these scintillation events is clear, and it is more precise in time than other scintillation indices under consideration.

Now, let us consider the data from high-latitude region, where scintillations are more frequent. **Figures 5** and **6** are similar to **Figure 4** and show the results derived from the 50-Hz data at stations EDM (53,35° N, 247,02° W) and GJO (68,63° N, 254,15° W). Both stations belong to the Canadian High Arctic Ionospheric Network (CHAIN) [29] and equipped with the same type SEPTENTRIO PolaRxS GNSS receivers [30]. The station EDM is still within mid-latitudes (however in Canada it strictly depends on current geomagnetic conditions), but the station GJO is in high-latitude region.

**3.2 Time resolution comparison**

**Figure 5.**

**15**

and show high correlation with *σφ* [3].

indices which are calculated based on 0.1–10 Hz data.

*The same as in* **Figure 4***, but for the high-latitude GJO station (Canada).*

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

The time resolution of input data is very important to detect scintillations. For example, the work [12] showed the significant sampling rate influence on *ROTI*. Indeed, the minimal size of the refractive irregularities is about the first Fresnel zone size (300–400 m at GNSS frequencies band). Such irregularities сan cause both refractive and diffractive variations not only in the input carrier phase data but also in the ionospheric TEC and its derivatives as well as variations of *S4* and *σφ*

The smaller irregularities (from tens of meters to 100–300 m) are mostly considered to provoke the diffractive amplitude and phase variations. To detect them the highest time rate possible is needed (higher that 10 Hz). Diffractive phenomena can cause the phase scintillations that are usually accompanied by the intense amplitude fluctuations**.** These can be detected by *σφ* and *S4* indices. When the diffractive Fresnel irregularities dominate, CNR and/or *S4* can vary significantly

Several kilometer size irregularities usually cause the refractive scintillations of 0.01–0.1 Hz. When such irregularities dominate, *S4* does not vary significantly and almost has no correlation to *ROTI*, *DROTI*, and even to *σφ*. Scintillations of refractive origin are better observed with the sharp TEC variations (i.e., by means of ROTI and DROTI) and with *σφ* [3, 5]. There are studies focused on the scintillation

The scintillation events are detected at both sites in the same time interval by all the considered indices: at EDM with PRN 30, PRN 26, and PRN 15 (**Figure 5**) and at GJO with PRN 06 (**Figure 6**).

It is seen that the weaker scintillation, the weaker the response of D2fi and *σφ*, which is not surprising as both indices are calculated from the same phase ranging data. Note that *σφ* index quality depends on the phase de-trending and filtering procedure. This could bring the artificial effect that is seen at 19.87 UT at **Figure 6c** (left column).

The comparison of different indices allows us to reveal the prevalence of phase or amplitude scintillations. In our case (**Figure 5**) the obvious difference in S4 and *σφ* behavior is seen for PRN 30, PRN 26, and PRN 15. Amplitude scintillations prevail at the ray path from PRN 30 (S4 exceeds 0.15, **Figure 5c**). On the other hand, phase scintillations are predominant at the ray paths from PRN 26 and 15 (*σφ* index reaches 0.2 but S4 index does not exceed 0.02 at the same time).

To sum up, **Figures 4**–**6** prove the following: (a) D2fi peaks are caused by scintillation events (as there are also responses in other scintillation indices though less precise) and (b) that the D2fi index shows more sensitivity to phase scintillations.

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

**Figure 5.** *The same as in* **Figure 4***, but for the high-latitude GJO station (Canada).*

#### **3.2 Time resolution comparison**

The time resolution of input data is very important to detect scintillations. For example, the work [12] showed the significant sampling rate influence on *ROTI*. Indeed, the minimal size of the refractive irregularities is about the first Fresnel zone size (300–400 m at GNSS frequencies band). Such irregularities сan cause both refractive and diffractive variations not only in the input carrier phase data but also in the ionospheric TEC and its derivatives as well as variations of *S4* and *σφ* indices which are calculated based on 0.1–10 Hz data.

The smaller irregularities (from tens of meters to 100–300 m) are mostly considered to provoke the diffractive amplitude and phase variations. To detect them the highest time rate possible is needed (higher that 10 Hz). Diffractive phenomena can cause the phase scintillations that are usually accompanied by the intense amplitude fluctuations**.** These can be detected by *σφ* and *S4* indices. When the diffractive Fresnel irregularities dominate, CNR and/or *S4* can vary significantly and show high correlation with *σφ* [3].

Several kilometer size irregularities usually cause the refractive scintillations of 0.01–0.1 Hz. When such irregularities dominate, *S4* does not vary significantly and almost has no correlation to *ROTI*, *DROTI*, and even to *σφ*. Scintillations of refractive origin are better observed with the sharp TEC variations (i.e., by means of ROTI and DROTI) and with *σφ* [3, 5]. There are studies focused on the scintillation

correlation between the D2fi index and *σφ* variations is seen for all the scintillation events and for all considered satellites, including the weakest event for PRN 04 (**Figure 4b** and **c**). Nevertheless, the peaks of the D2fi index are pronounced more sharply for all the cases. The correlation between the D2fi index and *S4* variations is worse. There is a general similarity in behavior of these parameters, but *S4* distribution is rather noisy and contains several peaks which do not coincide in time with

The worst correlation is between the D2fi index and *DROTI* for all the cases under consideration (**Figure 4b** and **e**). The form of *DROTI* envelope significantly differs from the envelope of the D2fi index. To add, *DROTI* observations are rather noisy. Almost no *DROTI* response is seen for the SV PRN 27 (**Figure 4e**, middle panel). The small-scale ionospheric irregularities do not provoke significant TEC response [28]. Consequently, even weaker response can be expected in TEC-derived indices like *DROTI*, which is probably the case of **Figure 4e**.

Let us consider the advantages of the D2fi index in comparison to other indices by the example in **Figure 4**. First, it marks the sharper and more precise in time response to small-scale turbulences than other indices. Furthermore, only one GPS frequency is needed to obtain the D2fi index. Thus, it avoids the possible impact from the inter-frequency noises and *L1*-aiding technique features [23]**.** Third, as the D2fi index is calculated from either *L1* or *L2* phase data, it does not require any additional preprocessing and does not depend on the data processing technique [8]. Finally, another advantage of the D2fi index is its high sensitivity. We recall that mid-latitudes are usually considered as the region where the scintillation occurrence is null except during geomagnetic disturbances. Even for the presented case, the scintillation intensity is very low (S4 is not higher than 0.1, **Figure 4d**). Nevertheless, the D2fi index response on these scintillation events is clear, and it is more

Now, let us consider the data from high-latitude region, where scintillations are more frequent. **Figures 5** and **6** are similar to **Figure 4** and show the results derived from the 50-Hz data at stations EDM (53,35° N, 247,02° W) and GJO (68,63° N, 254,15° W). Both stations belong to the Canadian High Arctic Ionospheric Network (CHAIN) [29] and equipped with the same type SEPTENTRIO PolaRxS GNSS receivers [30]. The station EDM is still within mid-latitudes (however in Canada it strictly depends on current geomagnetic conditions), but the station GJO is in

The scintillation events are detected at both sites in the same time interval by all the considered indices: at EDM with PRN 30, PRN 26, and PRN 15 (**Figure 5**) and at

It is seen that the weaker scintillation, the weaker the response of D2fi and *σφ*, which is not surprising as both indices are calculated from the same phase ranging data. Note that *σφ* index quality depends on the phase de-trending and filtering procedure. This could bring the artificial effect that is seen at 19.87 UT at **Figure 6c**

The comparison of different indices allows us to reveal the prevalence of phase or amplitude scintillations. In our case (**Figure 5**) the obvious difference in S4 and *σφ* behavior is seen for PRN 30, PRN 26, and PRN 15. Amplitude scintillations prevail at the ray path from PRN 30 (S4 exceeds 0.15, **Figure 5c**). On the other hand, phase scintillations are predominant at the ray paths from PRN 26 and 15 (*σφ* index reaches 0.2 but S4 index does not exceed 0.02 at the same time). To sum up, **Figures 4**–**6** prove the following: (a) D2fi peaks are caused by scintillation events (as there are also responses in other scintillation indices though less precise) and (b) that the D2fi index shows more sensitivity to phase

precise in time than other scintillation indices under consideration.

high-latitude region.

(left column).

scintillations.

**14**

GJO with PRN 06 (**Figure 6**).

the peaks of the D2fi index (**Figure 4b** and **d**).

*Satellites Missions and Technologies for Geosciences*

**Figure 6.** *The same as in Figure 4, but for the high-latitude EDM station (Canada).*

indices use based on the data of high-latitude receivers. For instance, the sDPR index was introduced in [15].

data shows both the highest noise level and the additional regular trend. The lowfrequency trends are mostly removed from the time series of higher sampling rate. In case of the highest data rate (50 Hz), the background values of D2fi do not exceed 0.4 rad/s\*s (**Figure 7a**). For the lower data rate (10 Hz), the weak regular trend appears, and the background noise increases to 0.6 rad/s\*s (**Figure 7b**). The D2fi variations increase 4–5 times and exceed 2–3 rad/s\*s in the last case (1 Hz data,

*The D2fi index in case of 50 Hz data sampling rate (a), 10 Hz data sampling rate (b), and 1 Hz data*

*sampling rate (c) for PRN 04, PRN 15, and PRN 27 on June 22, 2015, at ISTP station.*

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

Apart from the ionospheric scintillations, one of the common sources of the phase fluctuations is the multipath effect. The majority of the multipath-induced fluctuations are observed at lower elevation angles. It is also not a thorough determination of multipath as it is possible to observe it at the higher elevations as well [1]. Thus**,** we should test if the scintillation events revealed above are related to multipath and/or blocked signal effects. Usually**,** the multipath-induced phase variations are caused by the repeating events due to local reflection or diffuse scattering. The picture of such events repeats from day to day at the same location. At the same time, the picture of such "scintillations" has the regular time shift about 16 s from one day to another due to GPS orbits daily motion [17]. This means that to determine whether the scintillation candidate events are caused by repeating local multipath effects, the raw data for the day before and after the scintillation should be analyzed. **Figure 8** illustrates such the analysis for 50 Hz data on June 21, June 22,

No significant phase scintillations on the day before (June 21, **Figure 8**, left column) and/or after (June 23, **Figure 8**, right column) were observed. In contrast, there were the sharp and rapid variations of the second-order derivative of the

and June 23, 2015, for PRN 04, PRN15, and PRN27.

**Figure 7c**).

**17**

**Figure 7.**

Usually, the irregularities of different scales are present in the ionosphere simultaneously**.** It can occur during the volcanic eruptions, powerful explosions, rocket launching**,** under disturbed geomagnetic conditions, etc. [4]. The ionospheric irregularities can move with the quiet different velocities and in different directions. The 1 Hz or lower time resolution data does not allow us to reveal if the ionospheric event was caused by the diffractive irregularities of hundreds of meters or by the larger refractive irregularities of tens of kilometers.

We suggest that the high data sampling rate such as 10 Hz and higher provides the opportunity to reveal and analyze the weak small-scale ionospheric irregularities. To test this assumption, we compared 1, 10, and 50 Hz time series of the D2fi index for the same events and under the same geomagnetic storm conditions. **Figure 7** shows the results of comparison for PRN 04, PRN15, and PRN27 at ISTP station on June 22, 2015, during the main phase of the geomagnetic storm (**Figure 3**).

The D2fi index obtained from 1 Hz GPS data does not reveal any scintillation event for all three satellites (**Figure 7c**). In contrast**,** the time series obtained from 10 Hz data show the clear peaks for the SV PRN 15 and PRN 27 (**Figure 7b**), but not for the weakest event for SV PRN 04 (**Figure 7b**, left). The peaks of 50 Hz time series are the most pronounced for all the satellites (**Figure 7a**). Note that the 1 Hz

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

#### **Figure 7.**

indices use based on the data of high-latitude receivers. For instance, the sDPR

Usually, the irregularities of different scales are present in the ionosphere simultaneously**.** It can occur during the volcanic eruptions, powerful explosions, rocket launching**,** under disturbed geomagnetic conditions, etc. [4]. The ionospheric irregularities can move with the quiet different velocities and in different directions. The 1 Hz or lower time resolution data does not allow us to reveal if the ionospheric event was caused by the diffractive irregularities of hundreds of meters

We suggest that the high data sampling rate such as 10 Hz and higher provides the opportunity to reveal and analyze the weak small-scale ionospheric irregularities. To test this assumption, we compared 1, 10, and 50 Hz time series of the D2fi index for the same events and under the same geomagnetic storm conditions. **Figure 7** shows the results of comparison for PRN 04, PRN15, and PRN27 at ISTP

The D2fi index obtained from 1 Hz GPS data does not reveal any scintillation event for all three satellites (**Figure 7c**). In contrast**,** the time series obtained from 10 Hz data show the clear peaks for the SV PRN 15 and PRN 27 (**Figure 7b**), but not for the weakest event for SV PRN 04 (**Figure 7b**, left). The peaks of 50 Hz time series are the most pronounced for all the satellites (**Figure 7a**). Note that the 1 Hz

station on June 22, 2015, during the main phase of the geomagnetic storm

or by the larger refractive irregularities of tens of kilometers.

*The same as in Figure 4, but for the high-latitude EDM station (Canada).*

*Satellites Missions and Technologies for Geosciences*

index was introduced in [15].

(**Figure 3**).

**16**

**Figure 6.**

*The D2fi index in case of 50 Hz data sampling rate (a), 10 Hz data sampling rate (b), and 1 Hz data sampling rate (c) for PRN 04, PRN 15, and PRN 27 on June 22, 2015, at ISTP station.*

data shows both the highest noise level and the additional regular trend. The lowfrequency trends are mostly removed from the time series of higher sampling rate.

In case of the highest data rate (50 Hz), the background values of D2fi do not exceed 0.4 rad/s\*s (**Figure 7a**). For the lower data rate (10 Hz), the weak regular trend appears, and the background noise increases to 0.6 rad/s\*s (**Figure 7b**). The D2fi variations increase 4–5 times and exceed 2–3 rad/s\*s in the last case (1 Hz data, **Figure 7c**).

Apart from the ionospheric scintillations, one of the common sources of the phase fluctuations is the multipath effect. The majority of the multipath-induced fluctuations are observed at lower elevation angles. It is also not a thorough determination of multipath as it is possible to observe it at the higher elevations as well [1]. Thus**,** we should test if the scintillation events revealed above are related to multipath and/or blocked signal effects. Usually**,** the multipath-induced phase variations are caused by the repeating events due to local reflection or diffuse scattering. The picture of such events repeats from day to day at the same location. At the same time, the picture of such "scintillations" has the regular time shift about 16 s from one day to another due to GPS orbits daily motion [17]. This means that to determine whether the scintillation candidate events are caused by repeating local multipath effects, the raw data for the day before and after the scintillation should be analyzed. **Figure 8** illustrates such the analysis for 50 Hz data on June 21, June 22, and June 23, 2015, for PRN 04, PRN15, and PRN27.

No significant phase scintillations on the day before (June 21, **Figure 8**, left column) and/or after (June 23, **Figure 8**, right column) were observed. In contrast, there were the sharp and rapid variations of the second-order derivative of the

clearer the peaks of the D2fi index, and the weaker both the noise background and the low-frequency trend. The comparison between the D2fi index and DROTI, *S4*, and *σφ* showed that they have different "sensitivities." Each index has its own "critical" sensitivity for the particular ionospheric turbulences depending on the data sampling rate and preprocessing procedures. The advantage of the new D2fi index is that it is easily derived from the single-frequency carrier phase data. It provides both the reliable detection of the ionospheric scintillation and the phase time series de-trending with no complex data preprocessing. The new index can be applied as an independent scintillation indicator or as an additional tool together

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

This work was supported by the Russian Federation President Grant No. MK-3265.2019.5 and by grant No. 18-05-00343 from the Russian Foundation for Basic Research. LANCE acknowledges partial support from CONACyT LN-299022, CONACyT PN 2015-173, and CONACyT-AEM Grant 2017-2101-292684. ISTP staton data were recorded by the Angara Multiaccess Center facilities (http://ckpangara.iszf.irk.ru) at ISTP SB RAS within the base financing of FR program II.16.

Vladislav Demyanov is the principal author and the corresponding author of this

book chapter. The text and the figures presented in this book chapter were not

\*, Maria A. Sergeeva2,3

Institute of Solar-Terrestrial Physics, Irkutsk, Russia

Nacional Autonoma de Mexico, Michoacan, Mexico

\*Address all correspondence to: vv.emyanov@gmail.com

Autonoma de Mexico, Michoacan, Mexico

provided the original work is properly cited.

1 Laboratory of Elaboration of New Methods for Atmosphere Radio Diagnostics,

2 SCiESMEX, LANCE, Instituto de Geofisica, Unidad Michoacan, Universidad

3 CONACYT, Instituto de Geofisica, Unidad Michoacan, Universidad Nacional

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

with other scintillation indices.

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

**Acknowledgements**

**Conflict of interest**

**Author details**

**19**

Vladislav V. Demyanov<sup>1</sup>

and Anna S. Yasyukevich<sup>1</sup>

published anywhere else before.

**Figure 8.** *The D2fi index during June 21–23, 2015, for satellites PRN04 (a), PRN15 (b), PRN27 (c).*

carrier phase on June 22, 2015, for all the satellites. This fact proves that the phase scintillation events observed on June 22, 2015, are not related to the multipath effect. Thus**,** the above mentioned phase scintillation events probably have the ionospheric origin.

#### **4. Conclusions**

The performance of the well-known "standard" ionospheric scintillation indices ROTI, DROTI, *S4*, *σφ*, and the new scintillation index D2fi that is the second-order derivative of the GPS signal carrier phase was analyzed in this study. The features of GNSS receivers and antennas that can have an effect on this performance were considered. The benefits and limitations of the indices were discussed.

The overall accuracy of the GNSS carrier phase measurements is limited by both thermal and external noises and significantly depends on the GNSS hardware and software presets and architecture. The accuracy of the carrier phase measurements can be improved if the particular specification is used for GNSS equipment suggested for the ionospheric studies. This particular specification means that the narrowband code delay discriminator, the large code rate for the open-access GNSS signals, the expanded front-end bandpass of the RFC, the low-noise preamplifier, and the specific pattern antenna should be specified for the ionospheric study.

In the present study, the new index D2fi is proved to be an effective tool to detect the small-scale ionospheric irregularities. It was shown that the sensitivity of the D2fi index depends on the data sampling rate. The higher the sampling rate, the *GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

clearer the peaks of the D2fi index, and the weaker both the noise background and the low-frequency trend. The comparison between the D2fi index and DROTI, *S4*, and *σφ* showed that they have different "sensitivities." Each index has its own "critical" sensitivity for the particular ionospheric turbulences depending on the data sampling rate and preprocessing procedures. The advantage of the new D2fi index is that it is easily derived from the single-frequency carrier phase data. It provides both the reliable detection of the ionospheric scintillation and the phase time series de-trending with no complex data preprocessing. The new index can be applied as an independent scintillation indicator or as an additional tool together with other scintillation indices.

#### **Acknowledgements**

This work was supported by the Russian Federation President Grant No. MK-3265.2019.5 and by grant No. 18-05-00343 from the Russian Foundation for Basic Research. LANCE acknowledges partial support from CONACyT LN-299022, CONACyT PN 2015-173, and CONACyT-AEM Grant 2017-2101-292684. ISTP staton data were recorded by the Angara Multiaccess Center facilities (http://ckpangara.iszf.irk.ru) at ISTP SB RAS within the base financing of FR program II.16.

#### **Conflict of interest**

Vladislav Demyanov is the principal author and the corresponding author of this book chapter. The text and the figures presented in this book chapter were not published anywhere else before.

#### **Author details**

carrier phase on June 22, 2015, for all the satellites. This fact proves that the phase scintillation events observed on June 22, 2015, are not related to the multipath effect. Thus**,** the above mentioned phase scintillation events probably have the

*The D2fi index during June 21–23, 2015, for satellites PRN04 (a), PRN15 (b), PRN27 (c).*

*Satellites Missions and Technologies for Geosciences*

The performance of the well-known "standard" ionospheric scintillation indices ROTI, DROTI, *S4*, *σφ*, and the new scintillation index D2fi that is the second-order derivative of the GPS signal carrier phase was analyzed in this study. The features of GNSS receivers and antennas that can have an effect on this performance were

The overall accuracy of the GNSS carrier phase measurements is limited by both thermal and external noises and significantly depends on the GNSS hardware and software presets and architecture. The accuracy of the carrier phase measurements

considered. The benefits and limitations of the indices were discussed.

can be improved if the particular specification is used for GNSS equipment suggested for the ionospheric studies. This particular specification means that the narrowband code delay discriminator, the large code rate for the open-access GNSS signals, the expanded front-end bandpass of the RFC, the low-noise preamplifier, and the specific pattern antenna should be specified for the ionospheric study. In the present study, the new index D2fi is proved to be an effective tool to detect the small-scale ionospheric irregularities. It was shown that the sensitivity of the D2fi index depends on the data sampling rate. The higher the sampling rate, the

ionospheric origin.

**Figure 8.**

**4. Conclusions**

**18**

Vladislav V. Demyanov<sup>1</sup> \*, Maria A. Sergeeva2,3 and Anna S. Yasyukevich<sup>1</sup>

1 Laboratory of Elaboration of New Methods for Atmosphere Radio Diagnostics, Institute of Solar-Terrestrial Physics, Irkutsk, Russia

2 SCiESMEX, LANCE, Instituto de Geofisica, Unidad Michoacan, Universidad Nacional Autonoma de Mexico, Michoacan, Mexico

3 CONACYT, Instituto de Geofisica, Unidad Michoacan, Universidad Nacional Autonoma de Mexico, Michoacan, Mexico

\*Address all correspondence to: vv.emyanov@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

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[2] Fanis M, Stathis S. PLL bandwidth and noise in 100 Hz GPS measurements. GPS Solutions. 2015;**19**:173-185. DOI: 10.1007/s10291-014-0378-4

[3] Bhattacharrya A, Yen KC, Franke SJ. Deducing turbulence parameters from transionospheric scintillation measurements. Space Science Reviews. 1992;**61**:335-386

[4] Afraimovich EL, Perevalova NP, editors. GPS-Monitoring of Earth Upper Atmosphere. Irkutsk: Russian Academy of Sciences, Siberian Branch; 2006. 460p. ISBN 5-98277-033-7

[5] Pi X, Mannucci AJ, Lindqwister UJ, Ho CM. Monitoring of global ionospheric irregularities using the worldwide GPS-network. Geophysical Research Letters. 1997;**24**:2283-2286. DOI: 10.1029/97GL02273

[6] Van Dierendonck AJ, Klobuchar J, Hua Q. Ionospheric scintillation monitoring using commercial single frequency C/A code receivers. In: Proceedings of ION GPS 1993; Salt Lake City, Utah. 1993. pp. 1333-1342

[7] Ghoddousi-Fard R. Impact of receiver and constellation on high rate GNSS phase rate measurements to monitor ionospheric irregularities. Advances in Space Research. 2017; **60**(9):1968-1977

[8] Mushini SC, Jayachandran PT, Langley RB, MacDougall JW, Pokhotelov D. Improved amplitude-and phase-scintillation indices derived from wavelet detrended high-latitude GPS data. GPS Solutions. 2012;**16**(3):363-373

[9] Priyadarshi S, Zhang QH, Thomas EG, Spogli L, Cesaroni C. Polar traveling ionospheric disturbances inferred with the B-spline method and associated scintillations in the southern hemisphere. Advances in Space Research. 2018;**62**(11):3249-3266

acceleration: A new important parameter in GPS occultation

1996. p. 556

file/7021690472.pdf

icwg/IS-GPS-705A.pdf

0-471-70647-7

pp. 915-924

s00190-018-1172-9

**21**

PLANS. 1996. pp. 672-678

technology. GPS Solutions. 2010;**14**:3-11

*DOI: http://dx.doi.org/10.5772/intechopen.90078*

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices*

[25] Yasyukevich YV. 2017. The 50 Hz JAVAD Data Set for the Case Study*.* Available from: 10.5281/zenodo.848325

[26] Yasyukevich YuV, Vesnin AM, Perevalova NP. SibNet–Siberian global navigation satellite system network: Current state. Solar-Terrestrial Physics. 2018;**4**(4):63-72. DOI: 10.12737/stp-

[27] Piersanti M, Alberti T, Bemporad A, Berrilli F, Bruno R, Capparelli V, et al.

geoeffective solar event of 21 June 2015:

plasmasphere, and ionosphere systems.

Comprehensive analysis of the

Effects on the magnetosphere,

Solar Physics. 2017;**292**(11):169

[28] Perevalova NP, Sankov VA, Astafyeva EI, Zhupityaeva АS. Threshold magnitude for ionospheric TEC response to earthquakes. Journal of Atmospheric and Solar-Terrestrial

[29] Jayachandran PT, Langley RB, MacDougall JW, Mushini SC,

Pokhotelov D, Hamza AM, et al. The Canadian high arctic ionospheric network (CHAIN). Radio Science. 2009;

Physics. 2014;**108**:77-90

**44**:RS0A03. DOI: 10.1029/

[30] Bougard B, Sleewaegen JM, Spogli L, Veettil SV, Monico JF. CIGALA: Challenging the solar maximum in Brazil with PolaRxS. In: Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of

Navigation 2011, ION GNSS. Vol. 2011.

2008RS004046

2011. pp. 2572-2579

[Accessed: 30 May 2019]

44201809

[17] Kaplan ED. Understanding GPS: Principles and Applications. Boston, USA and London, UK: Artech House;

[18] MOPS Based Procedure for Minimum Recommended Testing of LightSquared RFI to GPS Aviation Receivers: Appendix A. 2017; p. 220. Available from: https://ecfsapi.fcc.gov/

[19] Global Positioning Systems Directorate Systems Engineering and Integration Interface Specification: IS-GPS-200J. 2018. Electronic resource. Available from: https://www.gps.gov/ technical/icwg/IS-GPS-200J.pdf

[20] Global Positioning System Wing Systems Engineering and Integration Interface Specification. IS-GPS-705, Revision A: Navstar GPS Space Segment/User Segment L5 Interfaces. 2010. Electronic resource. Available from: https://www.gps.gov/technical/

[21] Tsui JB. Fundamentals of Global Positioning System Receivers : A Software Approach. 2nd ed. Hoboken, New Jersey, USA: John Wiley & Sons, Inc., Publication; 2005. 605p. ISBN

[22] Van Nee R. Multipath effects on GPS code phase measurements. In: Proceedings of ION GPS. 1991.

[23] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/

[24] Braasch MS. GPS multipath model validation. In: Proceedings of ION

[10] Jayanta KR. Mitigation of GPS code and carrier phase multipath effects using a multi-antenna system [thesis]. Calgary, Alberta: The University of Calgary; 2000

[11] Qiongqiong J, Renbiao W, Wenyi W, Dan L, Lu W, Jie L. Multipath interference mitigation in GNSS via WRELAX. GPS Solutions. 2017;**21**:487-498. DOI: 10.1007/ s10291-016-0538-9

[12] Jacobsen KS. The impact of different sampling rates and calculation time intervals on ROTI values. Journal of Space Weather Space Climate. 2014;**4**: A33. DOI: 10.1051/swsc/2014031

[13] Ledvina BM, Makela JJ, Kintner PM. First observations of intense GPS L1 amplitude scintillations at multitude. Geophysical Research Letters. 2002;**29** (14):4. DOI: 10.1029/2002GL014770. Available from: https://agupubs. onlinelibrary.wiley.com/doi/epdf/ 10.1029/2002GL014770

[14] Kintner PM, Kil H, de Paula E. Fading time scales associated with GPS signals and potential consequences. Radio Science. 2001;**36**(4):731-743

[15] Ghoddousi-Fard R, Prikryl P, Lahaye F. GPS phase difference variation statistics: A comparison between phase scintillation index and proxy indices. Advances in Space Research. 2013;**52**(8):1397-1405

[16] Pavelyev AG, Liou YA, Wickert J, Schmidt T, Pavelyev AA. Phase

*GNSS High-Rate Data and the Efficiency of Ionospheric Scintillation Indices DOI: http://dx.doi.org/10.5772/intechopen.90078*

acceleration: A new important parameter in GPS occultation technology. GPS Solutions. 2010;**14**:3-11

**References**

[1] McCaffrey AM, Jayachandran PT. Spectral characteristics of auroral region scintillation using 100 Hz sampling. GPS Solutions. 2017;**21**:1883-1894. DOI:

*Satellites Missions and Technologies for Geosciences*

[9] Priyadarshi S, Zhang QH,

hemisphere. Advances in Space Research. 2018;**62**(11):3249-3266

[11] Qiongqiong J, Renbiao W, Wenyi W, Dan L, Lu W, Jie L. Multipath interference mitigation in GNSS via WRELAX. GPS Solutions. 2017;**21**:487-498. DOI: 10.1007/

Calgary; 2000

s10291-016-0538-9

10.1029/2002GL014770

[14] Kintner PM, Kil H, de Paula E. Fading time scales associated with GPS signals and potential consequences. Radio Science. 2001;**36**(4):731-743

[15] Ghoddousi-Fard R, Prikryl P, Lahaye F. GPS phase difference variation statistics: A comparison between phase scintillation index and proxy indices. Advances in Space Research. 2013;**52**(8):1397-1405

[16] Pavelyev AG, Liou YA, Wickert J, Schmidt T, Pavelyev AA. Phase

Thomas EG, Spogli L, Cesaroni C. Polar traveling ionospheric disturbances inferred with the B-spline method and associated scintillations in the southern

[10] Jayanta KR. Mitigation of GPS code and carrier phase multipath effects using a multi-antenna system [thesis]. Calgary, Alberta: The University of

[12] Jacobsen KS. The impact of different sampling rates and calculation time intervals on ROTI values. Journal of Space Weather Space Climate. 2014;**4**: A33. DOI: 10.1051/swsc/2014031

[13] Ledvina BM, Makela JJ, Kintner PM. First observations of intense GPS L1 amplitude scintillations at multitude. Geophysical Research Letters. 2002;**29** (14):4. DOI: 10.1029/2002GL014770. Available from: https://agupubs. onlinelibrary.wiley.com/doi/epdf/

[2] Fanis M, Stathis S. PLL bandwidth and noise in 100 Hz GPS measurements. GPS Solutions. 2015;**19**:173-185. DOI:

[3] Bhattacharrya A, Yen KC, Franke SJ. Deducing turbulence parameters from

measurements. Space Science Reviews.

[4] Afraimovich EL, Perevalova NP, editors. GPS-Monitoring of Earth Upper Atmosphere. Irkutsk: Russian Academy of Sciences, Siberian Branch; 2006.

[5] Pi X, Mannucci AJ, Lindqwister UJ,

[6] Van Dierendonck AJ, Klobuchar J, Hua Q. Ionospheric scintillation monitoring using commercial single frequency C/A code receivers. In: Proceedings of ION GPS 1993; Salt Lake

City, Utah. 1993. pp. 1333-1342

[7] Ghoddousi-Fard R. Impact of receiver and constellation on high rate GNSS phase rate measurements to monitor ionospheric irregularities. Advances in Space Research. 2017;

[8] Mushini SC, Jayachandran PT, Langley RB, MacDougall JW,

Pokhotelov D. Improved amplitude-and phase-scintillation indices derived from wavelet detrended high-latitude GPS data. GPS Solutions. 2012;**16**(3):363-373

**60**(9):1968-1977

**20**

10.1007/s10291-017-0664-z

10.1007/s10291-014-0378-4

transionospheric scintillation

460p. ISBN 5-98277-033-7

Ho CM. Monitoring of global ionospheric irregularities using the worldwide GPS-network. Geophysical Research Letters. 1997;**24**:2283-2286.

DOI: 10.1029/97GL02273

1992;**61**:335-386

[17] Kaplan ED. Understanding GPS: Principles and Applications. Boston, USA and London, UK: Artech House; 1996. p. 556

[18] MOPS Based Procedure for Minimum Recommended Testing of LightSquared RFI to GPS Aviation Receivers: Appendix A. 2017; p. 220. Available from: https://ecfsapi.fcc.gov/ file/7021690472.pdf

[19] Global Positioning Systems Directorate Systems Engineering and Integration Interface Specification: IS-GPS-200J. 2018. Electronic resource. Available from: https://www.gps.gov/ technical/icwg/IS-GPS-200J.pdf

[20] Global Positioning System Wing Systems Engineering and Integration Interface Specification. IS-GPS-705, Revision A: Navstar GPS Space Segment/User Segment L5 Interfaces. 2010. Electronic resource. Available from: https://www.gps.gov/technical/ icwg/IS-GPS-705A.pdf

[21] Tsui JB. Fundamentals of Global Positioning System Receivers : A Software Approach. 2nd ed. Hoboken, New Jersey, USA: John Wiley & Sons, Inc., Publication; 2005. 605p. ISBN 0-471-70647-7

[22] Van Nee R. Multipath effects on GPS code phase measurements. In: Proceedings of ION GPS. 1991. pp. 915-924

[23] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/ s00190-018-1172-9

[24] Braasch MS. GPS multipath model validation. In: Proceedings of ION PLANS. 1996. pp. 672-678

[25] Yasyukevich YV. 2017. The 50 Hz JAVAD Data Set for the Case Study*.* Available from: 10.5281/zenodo.848325 [Accessed: 30 May 2019]

[26] Yasyukevich YuV, Vesnin AM, Perevalova NP. SibNet–Siberian global navigation satellite system network: Current state. Solar-Terrestrial Physics. 2018;**4**(4):63-72. DOI: 10.12737/stp-44201809

[27] Piersanti M, Alberti T, Bemporad A, Berrilli F, Bruno R, Capparelli V, et al. Comprehensive analysis of the geoeffective solar event of 21 June 2015: Effects on the magnetosphere, plasmasphere, and ionosphere systems. Solar Physics. 2017;**292**(11):169

[28] Perevalova NP, Sankov VA, Astafyeva EI, Zhupityaeva АS. Threshold magnitude for ionospheric TEC response to earthquakes. Journal of Atmospheric and Solar-Terrestrial Physics. 2014;**108**:77-90

[29] Jayachandran PT, Langley RB, MacDougall JW, Mushini SC, Pokhotelov D, Hamza AM, et al. The Canadian high arctic ionospheric network (CHAIN). Radio Science. 2009; **44**:RS0A03. DOI: 10.1029/ 2008RS004046

[30] Bougard B, Sleewaegen JM, Spogli L, Veettil SV, Monico JF. CIGALA: Challenging the solar maximum in Brazil with PolaRxS. In: Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation 2011, ION GNSS. Vol. 2011. 2011. pp. 2572-2579

**Chapter 2**

**Abstract**

**1. Introduction**

**23**

The Influence of the Lower

Navigation Satellite Systems

*Boris Gavrilov, Yuriy Poklad and Iliya Ryakhovskiy*

Operating Conditions of

Ionospheric Disturbances on the

The study of the impact of ionospheric disturbances on the conditions of functioning of satellite communication and navigation systems and the development of methods to reduce this effect requires the development of methods for evaluating the parameters of ionospheric disturbances and their spatial and temporal distribution. Studies show that electron concentration disturbances, which can have a significant impact on the functioning of transionospheric radio channels, can occur both in the upper and lower ionosphere. At the same time, the methods of studying the dynamics of ionospheric disturbances in the lower ionosphere are not enough developed, and the interrelation of the lower and upper perturbations of the ionosphere is insufficiently studied. The aim of the work is an experimental study of disturbances of the upper and lower ionosphere in order to clarify the mechanisms of their relationship and study the spatiotemporal distribution of mid-latitude disturbances. The results obtained show that the contribution of the electron density disturbances in the D region to the total electron content of the ionosphere can be significant and considerably depends on the type of heliogeophysical processes.

**Keywords:** upper and lower ionosphere, traveling ionospheric disturbances, TEC,

The ionosphere is a region in the near-Earth space, where a number of technical systems vital for the life and safety of mankind (telecommunication, navigation, aircraft, surveillance systems, etc.) work continuously. These systems based on radio signals are sensitive to the varying electron density in the ionosphere. Its strong perturbations may cause failures and malfunction in these systems. So the investigation of the state and dynamics of the ionosphere and a prediction of

Ionospheric disturbances are closely related to geomagnetic storms, solar flares, and other natural and anthropogenic processes [1–6]. The effect that the lower and upper ionospheres have on the propagation of a radio signal depends on their frequency. The F region is critical for the propagation of high-frequency (HF) waves. State and dynamics of the D and E regions define the conditions of

VLF signals, magnetic storms, solar X-ray flares

irregularities and disturbances appearing are key questions.

#### **Chapter 2**

## The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation Satellite Systems

*Boris Gavrilov, Yuriy Poklad and Iliya Ryakhovskiy*

#### **Abstract**

The study of the impact of ionospheric disturbances on the conditions of functioning of satellite communication and navigation systems and the development of methods to reduce this effect requires the development of methods for evaluating the parameters of ionospheric disturbances and their spatial and temporal distribution. Studies show that electron concentration disturbances, which can have a significant impact on the functioning of transionospheric radio channels, can occur both in the upper and lower ionosphere. At the same time, the methods of studying the dynamics of ionospheric disturbances in the lower ionosphere are not enough developed, and the interrelation of the lower and upper perturbations of the ionosphere is insufficiently studied. The aim of the work is an experimental study of disturbances of the upper and lower ionosphere in order to clarify the mechanisms of their relationship and study the spatiotemporal distribution of mid-latitude disturbances. The results obtained show that the contribution of the electron density disturbances in the D region to the total electron content of the ionosphere can be significant and considerably depends on the type of heliogeophysical processes.

**Keywords:** upper and lower ionosphere, traveling ionospheric disturbances, TEC, VLF signals, magnetic storms, solar X-ray flares

#### **1. Introduction**

The ionosphere is a region in the near-Earth space, where a number of technical systems vital for the life and safety of mankind (telecommunication, navigation, aircraft, surveillance systems, etc.) work continuously. These systems based on radio signals are sensitive to the varying electron density in the ionosphere. Its strong perturbations may cause failures and malfunction in these systems. So the investigation of the state and dynamics of the ionosphere and a prediction of irregularities and disturbances appearing are key questions.

Ionospheric disturbances are closely related to geomagnetic storms, solar flares, and other natural and anthropogenic processes [1–6]. The effect that the lower and upper ionospheres have on the propagation of a radio signal depends on their frequency. The F region is critical for the propagation of high-frequency (HF) waves. State and dynamics of the D and E regions define the conditions of

propagation of low-frequency (LF) and very low-frequency (VLF) waves. Due to these reasons, HF and LF-VLF waves can be used to study F and D–E regions, respectively.

ΔTEC lat ð Þ¼ *;* long*;*t TEC lat ð Þ� *;* long*;*t M*,* (1)

where lat and long are latitude and longitude, t is UT, and M is the median value

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

Metronix MFS-07 magnetic field sensor. MFS-07 is a high-frequency induction coil magnetometer, and two are mounted along the geographic north-south (X) and east-west (Y) axes. The magnetic field sensors cover a wide frequency range from 1 mHz up to 50 kHz and a dynamic range >130 dB and have excellent low noise characteristics (5 � <sup>10</sup>�<sup>7</sup> nT/√Hz at 1000 Hz). ADU-07 unit and MFS-07 induction coils have a very stable transfer function over temperature and time. A GPS clock

Radio signals with a frequency below 30 kHz propagate in the Earth-ionosphere waveguide at distances of thousands and tens of thousands of kilometers with low attenuation. The relationship of the amplitude and phase of the VLF signal on the state of the D layer makes it possible to detect disturbances of the lower ionosphere in the path of the radio signal propagation. The transmitting stations were chosen so that the radio paths were under different azimuths over the territory of Europe. In our experiments the data of synchronous measurements and the signals from four transmitters were used: JXN (Gildeskål, Norway, 66.98° N, 13.87° E), GQD

(Anthorn, UK, 54.91° N, 2.27° W), GBZ (Skelton, UK, 54.73° N, 2.88° W), and NAA

Radio signals received at the MIK and Kiel (54.4° N, 10.1° E) observatories were compared with data on variations in TEC of the ionosphere according to the Scripps Orbit and Permanent Array Center (SOPAC) http://sopacold.ucsd.edu/dataBrowser. shtml and Madrigal (http://www.openmadrigal.org) databases. Since we were

*The location of VLF transmitters (gray circles), GPS receivers (asterisks), and MIK observatory.*

When analyzing the parameters of the VLF signal, it should be taken into account that their variations are associated with changes in the parameters of the upper wall of the waveguide along the entire path of signal propagation. In order to localize disturbances in the lower ionosphere, data on the signal propagation along the path were additionally used, crossing the paths JXN-MIK, GQD-MIK, and NAA-MIK. We chose the data of Kiel Longwave Monitor (http://www.lf-radio.de/) where signals were received from Norwegian transmitter JXN (66.96° N, 13.90° E).

of TEC for the previous 27 days for the point with coordinates lat and long. The equipment used to make the VLF signal measurements is the Metronix Analog/Digital Signal Conditioning Unit ADU-07 data logger connected to

provides the timing signals.

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

(Cutler, USA, 44.65° N, 67.28° W).

**Figure 1.**

**25**

The impact of the processes of interaction in the lithosphere, atmosphere, ionosphere, and magnetosphere system on the upper and lower ionosphere and radio wave propagation was studied for decades [7–10]. Total electron content (TEC) values determined from data of dual-frequency measurements of global navigation satellite system (GNSS) signals are widely used to study the state and dynamics of the ionosphere [1, 3, 5, 6]. TEC is an integral of electron density in a tube with a cross section of 1 m2 along the path of radio signal propagation from the navigation satellite to the receiver. It is assumed that TEC value mainly characterizes the state of the F region where (at least in quiet heliogeophysical conditions) the maximum electron density is observed.

Obtaining direct data on the state and dynamics of the lower ionosphere is a more complex experimental task, since at these altitudes the ionosondes, radars, and spacecraft practically do not work. The state of the lower ionosphere is often monitored by analyzing the characteristics of VLF (3–30 kHz) radio signals that propagate in the waveguide formed by the Earth's surface and the D region of the ionosphere. Variations in the amplitude and phase of VLF signals are mainly associated with changes in the state of the upper wall of the waveguide [11–13].

Despite the fact that both methods of ionosphere studying are quite effective, they are used separately as a rule, which does not allow investigating the relationship between the disturbances of the upper and lower ionosphere.

The focus of this article is an experimental study of the relationship between the perturbations of the upper and lower ionosphere.

The experiments were carried out during a strong geomagnetic storm and strong solar X-ray flares. Total electron content (TEC) data obtained from measurements of global navigation satellite system (GNSS) signals were used to study the F region. Information about the disturbances of the lower ionosphere is obtained by analyzing the amplitude and phase variations of VLF signals. Coordinated analysis of TEC and VLF signals is a powerful tool for studying interrelated processes in the D and F regions of the ionosphere. The results obtained strongly indicate their interconnected perturbations.

#### **2. Experimental setup**

Disturbances of the electron density and radio wave propagation in the D, E, and F regions of the ionosphere were investigated in the latitude range from 40° to 70° N and in the longitude range from 0° to 40° E. The "Mikhnevo" geophysical observatory (MIK, 54.9617° N, 37.7626° E) of the Institute of Geosphere Dynamics of the Russian Academy of Sciences (http://idg.chph.ras.ru/ru/watch/mikhnevo) continuously monitors the amplitudes and phases of signals in the frequency band from 9 to 30 kHz received from VLF stations located in Europe, Asia, and North America [14].

For the investigation of the upper ionosphere, we use the data of GPS receivers in Mikhnevo observatory and the worldwide GPS vertical TEC data included in the Madrigal database at MIT Haystack Observatory (http://www.openmadrigal.org/).

The Madrigal data contain TEC values with a time step of 5 min. These data were averaged over a 15-min interval and distributed over the 180° 360° grid with a step of 1°.

The deviation of TEC from the median value is calculated by the formula

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

$$\Delta \text{TEC}(\text{lat}, \text{long}, \mathbf{t}) = \text{TEC}(\text{lat}, \text{long}, \mathbf{t}) - \mathbf{M}, \tag{1}$$

where lat and long are latitude and longitude, t is UT, and M is the median value of TEC for the previous 27 days for the point with coordinates lat and long.

The equipment used to make the VLF signal measurements is the Metronix Analog/Digital Signal Conditioning Unit ADU-07 data logger connected to Metronix MFS-07 magnetic field sensor. MFS-07 is a high-frequency induction coil magnetometer, and two are mounted along the geographic north-south (X) and east-west (Y) axes. The magnetic field sensors cover a wide frequency range from 1 mHz up to 50 kHz and a dynamic range >130 dB and have excellent low noise characteristics (5 � <sup>10</sup>�<sup>7</sup> nT/√Hz at 1000 Hz). ADU-07 unit and MFS-07 induction coils have a very stable transfer function over temperature and time. A GPS clock provides the timing signals.

Radio signals with a frequency below 30 kHz propagate in the Earth-ionosphere waveguide at distances of thousands and tens of thousands of kilometers with low attenuation. The relationship of the amplitude and phase of the VLF signal on the state of the D layer makes it possible to detect disturbances of the lower ionosphere in the path of the radio signal propagation. The transmitting stations were chosen so that the radio paths were under different azimuths over the territory of Europe. In our experiments the data of synchronous measurements and the signals from four transmitters were used: JXN (Gildeskål, Norway, 66.98° N, 13.87° E), GQD (Anthorn, UK, 54.91° N, 2.27° W), GBZ (Skelton, UK, 54.73° N, 2.88° W), and NAA (Cutler, USA, 44.65° N, 67.28° W).

When analyzing the parameters of the VLF signal, it should be taken into account that their variations are associated with changes in the parameters of the upper wall of the waveguide along the entire path of signal propagation. In order to localize disturbances in the lower ionosphere, data on the signal propagation along the path were additionally used, crossing the paths JXN-MIK, GQD-MIK, and NAA-MIK. We chose the data of Kiel Longwave Monitor (http://www.lf-radio.de/) where signals were received from Norwegian transmitter JXN (66.96° N, 13.90° E).

Radio signals received at the MIK and Kiel (54.4° N, 10.1° E) observatories were compared with data on variations in TEC of the ionosphere according to the Scripps Orbit and Permanent Array Center (SOPAC) http://sopacold.ucsd.edu/dataBrowser. shtml and Madrigal (http://www.openmadrigal.org) databases. Since we were

**Figure 1.** *The location of VLF transmitters (gray circles), GPS receivers (asterisks), and MIK observatory.*

propagation of low-frequency (LF) and very low-frequency (VLF) waves. Due to these reasons, HF and LF-VLF waves can be used to study F and D–E regions,

Obtaining direct data on the state and dynamics of the lower ionosphere is a more complex experimental task, since at these altitudes the ionosondes, radars, and spacecraft practically do not work. The state of the lower ionosphere is often monitored by analyzing the characteristics of VLF (3–30 kHz) radio signals that propagate in the waveguide formed by the Earth's surface and the D region of the ionosphere. Variations in the amplitude and phase of VLF signals

Despite the fact that both methods of ionosphere studying are quite effective, they are used separately as a rule, which does not allow investigating the relation-

The focus of this article is an experimental study of the relationship between the

The experiments were carried out during a strong geomagnetic storm and strong solar X-ray flares. Total electron content (TEC) data obtained from measurements of global navigation satellite system (GNSS) signals were used to study the F region. Information about the disturbances of the lower ionosphere is obtained by analyzing the amplitude and phase variations of VLF signals. Coordinated analysis of TEC and VLF signals is a powerful tool for studying interrelated processes in the D and F

Disturbances of the electron density and radio wave propagation in the D, E, and F regions of the ionosphere were investigated in the latitude range from 40° to 70° N and in the longitude range from 0° to 40° E. The "Mikhnevo" geophysical observatory (MIK, 54.9617° N, 37.7626° E) of the Institute of Geosphere Dynamics of the Russian Academy of Sciences (http://idg.chph.ras.ru/ru/watch/mikhnevo) continuously monitors the amplitudes and phases of signals in the frequency band from 9 to 30 kHz received from VLF stations located in Europe, Asia, and North

For the investigation of the upper ionosphere, we use the data of GPS receivers in Mikhnevo observatory and the worldwide GPS vertical TEC data included in the Madrigal database at MIT Haystack Observatory (http://www.openmadrigal.org/). The Madrigal data contain TEC values with a time step of 5 min. These data were averaged over a 15-min interval and distributed over the 180° 360° grid with a

The deviation of TEC from the median value is calculated by the formula

are mainly associated with changes in the state of the upper wall of the

ship between the disturbances of the upper and lower ionosphere.

regions of the ionosphere. The results obtained strongly indicate their

perturbations of the upper and lower ionosphere.

The impact of the processes of interaction in the lithosphere, atmosphere, ionosphere, and magnetosphere system on the upper and lower ionosphere and radio wave propagation was studied for decades [7–10]. Total electron content (TEC) values determined from data of dual-frequency measurements of global navigation satellite system (GNSS) signals are widely used to study the state and dynamics of the ionosphere [1, 3, 5, 6]. TEC is an integral of electron density in a tube with a cross section of 1 m2 along the path of radio signal propagation from the navigation satellite to the receiver. It is assumed that TEC value mainly characterizes the state of the F region where (at least in quiet heliogeophysical conditions) the maximum

respectively.

electron density is observed.

*Satellites Missions and Technologies for Geosciences*

interconnected perturbations.

**2. Experimental setup**

America [14].

step of 1°.

**24**

waveguide [11–13].

interested in the interrelated perturbations of the upper and lower ionospheres, GPS stations located near the used VLF signal traces were chosen. The location of the transmitters and measuring stations is shown in **Figure 1**.

The strongest geomagnetic storm in the current solar cycle has been studied in sufficient detail. Ground-based and space-born measurements demonstrate the response of the ionosphere to the geomagnetic storm. Astafyeva et al. and Borries et al. [15, 16] presented the results of investigation of the effects of the St. Patrick's

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

*Variations of VLF signal amplitude on GQD, NAA-MIK paths and on JXN-Kiel path (http://www.lf-radio.*

Day ionospheric storm from the data of ground-based the GPS receivers,

**Figure 3.**

**Figure 4.**

**27**

*The ΔTEC value determined by Eq. (1) for 17.5 UT on 17 March 2015.*

*de/) from 17 to 18 UT on 17 March 2015.*

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

The possibility of detection of interconnected disturbances in the upper and lower ionosphere by GPS and VLF receivers is shown by the example of the study of the ionospheric effects of the magnetic storm on 17 March 2015 and the solar X-ray flash on 6 September 2017. These events were chosen because they caused significant changes in the ionosphere, but the mechanisms of generation and evolution of ionospheric inhomogeneities during magnetic storms and solar X-ray flares are very different, which should be manifested in the pattern of the reaction of the D and F layers of the ionosphere to these phenomena.

#### **3. St. Patrick's Day geomagnetic storm**

The storm began on 15 March 2015 as a series of mid-level solar flares culminating in a class C9 flare at 02:13 UT. At 04:05 UT on 17 March 2015, the Advanced Composition Explorer (ACE) satellite recorded a sharp increase in the solar wind speed up to 500 km/s. The lowest value of the disturbance storm time index Dst exceeded 200 nT, the auroral activity index AE exceeded 2200, and the planetary index of the geomagnetic activity Kp reached a value of 8. Such values of these indices make it possible to define the event on 17 March 2015 as an extreme magnetic storm, which caused a storm in the ionosphere.

#### **Figure 2.**

*Variations of TEC according to observatories mar6 and vis0 (top panel), VLF signal amplitude on NAA and GQD-MIK paths (bottom panel) on 17 March 2015.*

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

The strongest geomagnetic storm in the current solar cycle has been studied in sufficient detail. Ground-based and space-born measurements demonstrate the response of the ionosphere to the geomagnetic storm. Astafyeva et al. and Borries et al. [15, 16] presented the results of investigation of the effects of the St. Patrick's Day ionospheric storm from the data of ground-based the GPS receivers,

**Figure 3.**

interested in the interrelated perturbations of the upper and lower ionospheres, GPS stations located near the used VLF signal traces were chosen. The location of the

The possibility of detection of interconnected disturbances in the upper and lower ionosphere by GPS and VLF receivers is shown by the example of the study of the ionospheric effects of the magnetic storm on 17 March 2015 and the solar X-ray flash on 6 September 2017. These events were chosen because they caused significant changes in the ionosphere, but the mechanisms of generation and evolution of ionospheric inhomogeneities during magnetic storms and solar X-ray flares are very different, which should be manifested in the pattern of the reaction of the D and F

The storm began on 15 March 2015 as a series of mid-level solar flares culminating in a class C9 flare at 02:13 UT. At 04:05 UT on 17 March 2015, the Advanced Composition Explorer (ACE) satellite recorded a sharp increase in the solar wind speed up to 500 km/s. The lowest value of the disturbance storm time index Dst exceeded 200 nT, the auroral activity index AE exceeded 2200, and the planetary index of the geomagnetic activity Kp reached a value of 8. Such values of these indices make it possible to define the event on 17 March 2015 as an extreme

*Variations of TEC according to observatories mar6 and vis0 (top panel), VLF signal amplitude on NAA and*

transmitters and measuring stations is shown in **Figure 1**.

layers of the ionosphere to these phenomena.

*Satellites Missions and Technologies for Geosciences*

**3. St. Patrick's Day geomagnetic storm**

**Figure 2.**

**26**

*GQD-MIK paths (bottom panel) on 17 March 2015.*

magnetic storm, which caused a storm in the ionosphere.

*Variations of VLF signal amplitude on GQD, NAA-MIK paths and on JXN-Kiel path (http://www.lf-radio. de/) from 17 to 18 UT on 17 March 2015.*

**Figure 4.** *The ΔTEC value determined by Eq. (1) for 17.5 UT on 17 March 2015.*

ionosondes, and satellite missions. They reveal both a positive effect (TEC increase) at low- and mid-latitudes and positive and negative phases throughout all the latitudes. So, the results of these studies are mostly related to disturbances in the F region of the ionosphere. The effect of the magnetic storms in the lower ionosphere is less known due to the limited possibilities of ionosondes and incoherent scattering radars for the investigation of this region. Our data should allow to compare the results of the study of the F layer of the ionosphere with the effects observed in the lower ionosphere.

The upper panel of **Figure 2** shows the ionospheric variations of TEC calculated from data of GPS receivers located at stations vis0 and mar6. The lower panel shows the VLF signal amplitude variations on the NAA-MIK and GQD-MIK paths. TEC disturbances and VLF signal variations correspond to the main phase of the magnetic storm.

The **Figure 3** shows the change in the VLF signal amplitude on the JXN-Kiel path together with signals on the GQD-MIK and NAA-MIK paths. The maximum amplitude of all signals also corresponds to the main phase of the storm.

**Figure 4** shows the distribution of TEC deviation from the previous 27 days over Europe at 17:30 UT according to data of the Madrigal network. The Madrigal data contain TEC values with a time step of 5 min. These data were averaged over a 15 min interval and distributed over the geographic grid with a step of 1°.

It can be seen that the strongest ionospheric perturbations are localized at this time in the region of our measurements around the Kiel and along the GQD-Mikhnevo path.

#### **4. Ionospheric effects of the solar X-ray flares in September 2017**

The solar X-ray flares were chosen as another high-energy event, different from the magnetic storm by the mechanisms of influence on the ionosphere. The main perturbation agent of the ionosphere is X-ray and ultraviolet radiation.

Monitoring of VLF signals is conducted in the Mikhnevo since 2014. The most powerful solar X-ray flares for this period occurred in early September 2017. Two solar X-ray flares X2.2 at 09 UT and X9.3 at 12 UT were observed on 6 September 2017. 10 September 2017 was observed X8.3 flare. But at this time, our receivers in Mikhnevo and part of VLF paths were in the region of evening terminator. So its flare was not used in our analysis.

To analyze flare effects in the upper and lower ionosphere, we used the same GPS stations and the same VLF signal paths that were used at observing the effects of the magnetic storm on 17 March 2015. Note that all measuring points and radio paths were located in the territory illuminated by the flashes.

Graphs of vertical TEC variations at flare 6 September 2017 at 12 UT according to GPS receiver located at mar6 and vis0 stations are shown in **Figure 5a**. Variations in the amplitude and phase of the radio signal received in the Mikhnevo from two VLF radio transmitters (GQD and NAA) are shown in **Figure 5b** and **c**. The maximum response to the flare was observed at 12 UT as a simultaneous jump in the ΔTEC and in phase and amplitude of VLF signals.

One of the most common ways to describe the D region is the Ferguson-White model [17, 18]. According to this model, the altitude profile of the electron concen-

*(a) Variations of TEC according to GPS receiver mar6, (b) amplitude, and (c) phase of VLF signals on the*

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

*Ne z*ð Þ¼ <sup>1</sup>*:*<sup>49</sup> � <sup>10</sup><sup>7</sup> � exp ð Þ *<sup>β</sup>* � <sup>0</sup>*:*<sup>15</sup> *<sup>z</sup>* � *<sup>h</sup>*<sup>0</sup> � exp �0*:*15*h*<sup>0</sup> *cm*�<sup>3</sup> (2)

where *h'* is the referenced altitude of the ionosphere, *β* is the slope factor or sharpness of the electron concentration profile, and *h* is the current height. The approach proposed in [19] can be used to estimate variations of these ionospheric parameters during solar flares. In this work, the effects of solar energetic phenomena on the lower ionosphere using parameters of subionospherically

tration in the lower ionosphere is described by the equation:

*paths JXN-MIK, GQD-MIK, and NAA-MIK on 6 September 2017.*

**Figure 5.**

**29**

Comparing **Figures 2** and **5**, we can conclude that the growth of TEC during the X-ray flash was about 10 times less and the increase in the amplitude of the VLF signal was about 5 times greater than during the magnetic storm. To evaluate the effect of the X-ray flash on the additional ionization of the D layer, it is necessary to use theoretical models that allow to relate the parameters of the VLF signals to the change in the parameters of the lower ionosphere.

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

**Figure 5.**

ionosondes, and satellite missions. They reveal both a positive effect (TEC increase) at low- and mid-latitudes and positive and negative phases throughout all the latitudes. So, the results of these studies are mostly related to disturbances in the F region of the ionosphere. The effect of the magnetic storms in the lower ionosphere is less known due to the limited possibilities of ionosondes and incoherent scattering radars for the investigation of this region. Our data should allow to compare the results of the study of the F layer of the ionosphere with the effects observed in the

The upper panel of **Figure 2** shows the ionospheric variations of TEC calculated from data of GPS receivers located at stations vis0 and mar6. The lower panel shows the VLF signal amplitude variations on the NAA-MIK and GQD-MIK paths. TEC disturbances and VLF signal variations correspond to the main phase of the

The **Figure 3** shows the change in the VLF signal amplitude on the JXN-Kiel path together with signals on the GQD-MIK and NAA-MIK paths. The maximum ampli-

**Figure 4** shows the distribution of TEC deviation from the previous 27 days over Europe at 17:30 UT according to data of the Madrigal network. The Madrigal data contain TEC values with a time step of 5 min. These data were averaged over a 15-

It can be seen that the strongest ionospheric perturbations are localized at this

tude of all signals also corresponds to the main phase of the storm.

min interval and distributed over the geographic grid with a step of 1°.

time in the region of our measurements around the Kiel and along the GQD-

**4. Ionospheric effects of the solar X-ray flares in September 2017**

different from the magnetic storm by the mechanisms of influence on the ionosphere. The main perturbation agent of the ionosphere is X-ray and ultraviolet

Monitoring of VLF signals is conducted in the Mikhnevo since 2014. The most powerful solar X-ray flares for this period occurred in early September 2017. Two solar X-ray flares X2.2 at 09 UT and X9.3 at 12 UT were observed on 6 September 2017. 10 September 2017 was observed X8.3 flare. But at this time, our receivers in Mikhnevo and part of VLF paths were in the region of evening terminator. So its

To analyze flare effects in the upper and lower ionosphere, we used the same GPS stations and the same VLF signal paths that were used at observing the effects of the magnetic storm on 17 March 2015. Note that all measuring points and radio

Graphs of vertical TEC variations at flare 6 September 2017 at 12 UT according to GPS receiver located at mar6 and vis0 stations are shown in **Figure 5a**. Variations in the amplitude and phase of the radio signal received in the Mikhnevo from two VLF radio transmitters (GQD and NAA) are shown in **Figure 5b** and **c**. The maximum response to the flare was observed at 12 UT as a simultaneous jump in the

Comparing **Figures 2** and **5**, we can conclude that the growth of TEC during the X-ray flash was about 10 times less and the increase in the amplitude of the VLF signal was about 5 times greater than during the magnetic storm. To evaluate the effect of the X-ray flash on the additional ionization of the D layer, it is necessary to use theoretical models that allow to relate the parameters of the VLF signals to the

The solar X-ray flares were chosen as another high-energy event,

paths were located in the territory illuminated by the flashes.

ΔTEC and in phase and amplitude of VLF signals.

change in the parameters of the lower ionosphere.

lower ionosphere.

*Satellites Missions and Technologies for Geosciences*

magnetic storm.

Mikhnevo path.

radiation.

**28**

flare was not used in our analysis.

*(a) Variations of TEC according to GPS receiver mar6, (b) amplitude, and (c) phase of VLF signals on the paths JXN-MIK, GQD-MIK, and NAA-MIK on 6 September 2017.*

One of the most common ways to describe the D region is the Ferguson-White model [17, 18]. According to this model, the altitude profile of the electron concentration in the lower ionosphere is described by the equation:

$$\text{Ne}(\mathbf{z}) = \mathbf{1.49} \cdot \mathbf{10}^{\mathsf{T}} \cdot \exp\left( (\beta - \mathbf{0.15}) (\mathbf{z} - h') \right) \cdot \exp\left( -\mathbf{0.15} h' \right) \quad \left[ cm^{-3} \right] \tag{2}$$

where *h'* is the referenced altitude of the ionosphere, *β* is the slope factor or sharpness of the electron concentration profile, and *h* is the current height.

The approach proposed in [19] can be used to estimate variations of these ionospheric parameters during solar flares. In this work, the effects of solar energetic phenomena on the lower ionosphere using parameters of subionospherically propagating VLF signals were studied. This is done in two steps. At the first stage, initial values of *h'* and *β* are selected for this VLF signal propagation path from empirical models [20–22] that take into account the impact on the value of *h'* and *β* zenith angle of the sun, latitude, day of the year, and solar activity in the form of the Wolf number. In the second stage, the standard for estimation of the VLF signals propagation Long Wavelength Propagation Capability (LWPC) code is used to estimate the amplitude change in the exponential ionosphere. As a result, the values of *h'* and *β* as a function of the X-ray flux were obtained.

In this paper, a different approach is used to evaluate changes in the lower ionosphere caused by X-ray flashes. The key difference between our method and [19] is the approach to determining the parameters of the ionosphere. We obtain initial conditions by processing experimental data on the amplitude and phase of VLF signals under the action of X-rays. We have developed a method for restoring the altitude profile of electron concentration in the D region of the ionosphere by using the amplitude and phase characteristics of signals from VLF transmitters on a two-frequency path. To implement this technique, the signals of two VLF transmitters located at a distance of 32 km from each other were used. GQD and GBZ transmitters operate at frequencies of 22.1 and 19.58 kHz, respectively. Taking into account the length of the path of about 2600 km, we can assume that the signals

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

from these two stations are distributed along one two-frequency path.

recovery during X-ray flash of class X9.3 on 6 September 2017.

phase of VLF signals under the action of X-rays.

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

**Figure 7.**

**31**

*GQD и GBZ.*

The key difference between our method and [19] is the approach to determine the parameters of the undisturbed ionosphere. Statistical data do not take into account the impact on the ionosphere of factors not described by empirical models. We obtain initial conditions by processing experimental data on the amplitude and

Let us consider in detail this technique on the example of ionosphere parameter

**Figure 7** shows the experimental data on the variations of the amplitude and phase of the signals from GQD and GBZ stations. The bottom panel shows the X

*The calculated values of the amplitude (top panel) and phase (bottom panel) signals of the stations of*

The disadvantage of this approach is that the actual state of the ionosphere before a flash may differ from the model due to disturbances from such effects as magnetic storms and an increased X-ray flux.

#### **Figure 6.**

*Variations of the amplitude A\* (a) and phase P\* (b) of the signals from GQD and GBZ transmitters and X-ray flux (c) from GOES 15 satellite data (https://www.polarlicht-vorhersage.de/goes/2017-09-06\_110000\_ 2017-09-06\_130000.png) on 6 September 2017.*

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

In this paper, a different approach is used to evaluate changes in the lower ionosphere caused by X-ray flashes. The key difference between our method and [19] is the approach to determining the parameters of the ionosphere. We obtain initial conditions by processing experimental data on the amplitude and phase of VLF signals under the action of X-rays. We have developed a method for restoring the altitude profile of electron concentration in the D region of the ionosphere by using the amplitude and phase characteristics of signals from VLF transmitters on a two-frequency path. To implement this technique, the signals of two VLF transmitters located at a distance of 32 km from each other were used. GQD and GBZ transmitters operate at frequencies of 22.1 and 19.58 kHz, respectively. Taking into account the length of the path of about 2600 km, we can assume that the signals from these two stations are distributed along one two-frequency path.

The key difference between our method and [19] is the approach to determine the parameters of the undisturbed ionosphere. Statistical data do not take into account the impact on the ionosphere of factors not described by empirical models. We obtain initial conditions by processing experimental data on the amplitude and phase of VLF signals under the action of X-rays.

Let us consider in detail this technique on the example of ionosphere parameter recovery during X-ray flash of class X9.3 on 6 September 2017.

**Figure 7** shows the experimental data on the variations of the amplitude and phase of the signals from GQD and GBZ stations. The bottom panel shows the X

propagating VLF signals were studied. This is done in two steps. At the first stage, initial values of *h'* and *β* are selected for this VLF signal propagation path from empirical models [20–22] that take into account the impact on the value of *h'* and *β* zenith angle of the sun, latitude, day of the year, and solar activity in the form of the Wolf number. In the second stage, the standard for estimation of the VLF signals propagation Long Wavelength Propagation Capability (LWPC) code is used to estimate the amplitude change in the exponential ionosphere. As a result, the values

The disadvantage of this approach is that the actual state of the ionosphere before a flash may differ from the model due to disturbances from such effects as

*Variations of the amplitude A\* (a) and phase P\* (b) of the signals from GQD and GBZ transmitters and X-ray flux (c) from GOES 15 satellite data (https://www.polarlicht-vorhersage.de/goes/2017-09-06\_110000\_*

of *h'* and *β* as a function of the X-ray flux were obtained.

magnetic storms and an increased X-ray flux.

*Satellites Missions and Technologies for Geosciences*

**Figure 6.**

**30**

*2017-09-06\_130000.png) on 6 September 2017.*

flux in two spectral bands according to the Geostationary Operational Environmental Satellite (GOES) data.

Denote the X9.3 flash start time as *t*<sup>0</sup> = 11:52:20 UT and the time of maximum radiation as *t*max = 12:02:14 UT. Let variations of the amplitude and phase *dAi*(*t*) and *dPi*(*t*) be determined as

$$\begin{aligned} dA\_i(t) &= A\_i^\*\left(t\right) - A\_i^\*\left(t\_0\right) \\ dP\_i(t) &= P\_i^\*\left(t\right) - P\_i^\*\left(t\_0\right) \end{aligned} \tag{3}$$

*Ai h* 0 *t ; βt* � *Ai*ð*<sup>h</sup>*

*Pi h* 0 *t ; βt* � *Pi*ð*<sup>h</sup>*

 

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

 

signals.

**Figure 8.**

**33**

*are extended gray areas.*

*Eq. (2). The regions of existence of pairs of points (h0*

*h*<sup>0</sup> = 70.7 and *β* = 0.33 km�<sup>1</sup>

from 0.36 to 0.33 km�<sup>1</sup>

0

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

0

where *t*<sup>0</sup> < *t* < *t*max. This family of values is shown in **Figure 8** as black points. From **Figure 8** it is seen that the initial parameters of the ionosphere lie in a very narrow range. It is approximately 0.6 km in *h*<sup>0</sup> and 0.015 km�<sup>1</sup> in *β*. The dispersion of the ionospheric parameters at the time of maximum flare is approximately the same in *h*<sup>0</sup> and 10 times larger in *β* (about 0.15). This is due to the fact that the flash was sufficiently powerful and at the time of the maximum X-ray flux, the "rigidity" of the upper wall of the waveguide became so large that its further increase had virtually ceased to influence the amplitude-phase characteristics of the received

Further, for the initial parameters of the ionosphere that we calculated (i.e.,

parameters of the ionosphere for another time: during the decay phase of the X-ray flux, i.e., for time *t* > *t*max, and for the time before flash, i.e., *t* < *t*0. Thus, we can restore the time history of the parameters *h*<sup>0</sup> and *β* for the X-ray flash. These results are shown in **Figure 9b**. This figure clearly shows the advantage of our method. It can be seen how the parameters of the ionosphere changed before the flash from 11:30 to 11:55 UT. The parameter h' changed from 69.5 to 71 km and the *β* parameter

previous flare of class X2.2 that occurred at 9:00 UT. The same method was used for

*The regions of ionospheric parameters h*<sup>0</sup> *and β before the flash Eq. (1) and at maximum X-ray radiation*

0

*, β0) and (hmax*<sup>0</sup>

*, βmax) that satisfy the condition Eq. (4)*

<sup>0</sup>*; β*0Þ � *dAi*ð Þ*t*

<sup>0</sup>*; β*0Þ � *dPi*ð Þ*t*

 <sup>&</sup>lt;*δ<sup>A</sup>*

*,* (5)

 <sup>&</sup>lt;*δ<sup>P</sup>*

), we can use Eq. (5) to continue the calculation of the

. This is due to the relaxation of the ionosphere after the

where *Ai \** and *Pi \** are the measured amplitudes and phases of the signals of the GQD (*i* = 1) and GBZ (*i* = 2) transmitters shown in **Figure 6**.

On the other hand, the amplitude and phase of the signals of the GQD and GBZ transmitters depend on the parameters of the ionosphere *h*<sup>0</sup> and *β* on the signal propagation path. Suppose that for the whole GQD-/GBZ-Mikhnevo path, the parameters *h*<sup>0</sup> and *β* are the same. Then, the amplitude and phase values of the signals from the transmitters for all possible pairs of *h*<sup>0</sup> and *β* values were calculated using LWPC code. This code allows to calculate amplitude and phase of the VLF signal for the given path and the values of *h'* and *β* parameters. The calculations were carried out in the range of 50–90 km with 0.035 km increments for *h'* and 0.2– 0.95 km�<sup>1</sup> with 0.001 km�<sup>1</sup> increments for *β*. The ranges of values of *h*<sup>0</sup> and *β* were selected according to [20–22]. Thus, four matrices with the size of 1143 � 751 elements with values of amplitudes and phases of VLF signals versus *h*<sup>0</sup> and *β* parameters were obtained. Let us denote the calculated values of the amplitude and phase as *Ai*(*h*<sup>0</sup> *, β*) and *Pi*(*h*<sup>0</sup> *, β*), where *i* = 1 for GQD and *i* = 2 for GBZ transmitters. The graphical representation of these data is shown in **Figure 7**.

Let us denote the parameters of the ionosphere at a time *t*<sup>0</sup> as *h*<sup>0</sup> <sup>0</sup> and *β*<sup>0</sup> and at a time *t*max as *h*<sup>0</sup> *max* and *β*max. In the matrices *Ai*(*h*<sup>0</sup> *, β*) и *Pi*(*h*<sup>0</sup> *, β*), we can find all pairs of points (*h*<sup>0</sup> 0 , *β*0) and (*h'*max, *β*max) for which the difference between the values of amplitudes and phases for *t*<sup>0</sup> and *t*max coincides with that measured with given precision:

$$\begin{aligned} \left| A\_i \left( h\_{\text{max}}^{'}, \beta\_{\text{max}} \right) - A\_i(h\_0^{'}, \beta\_0) - dA\_i(t\_{\text{max}}) \right| &< \delta A \\ \left| P\_i \left( h\_{\text{max}}^{'}, \beta\_{\text{max}} \right) - P\_i(h\_0^{'}, \beta\_0) - dP\_i(t\_{\text{max}}) \right| &< \delta P \end{aligned} \tag{4}$$

where *δ A* = 0.12 dB and *δP* = 0.06 rad are the accuracy of estimation of the parameters of the ionosphere.

Based on the data [20–22], we assume that the parameters of the ionosphere at *t*<sup>0</sup> lie in the range of 68 < *h*<sup>0</sup> <sup>0</sup> < 77 km and 0.22 < *β*<sup>0</sup> < 0.35 and the parameters of the ionosphere at the *t*max lie in the range of 54 < *h*max' < 68 and 0.31 < *β*max < 0.95.

The regions of existence of pairs of points (*h*0', *β*0) and (*h*max', *β*max) that satisfy condition Eq. (4) are shown in **Figure 8** by extended gray areas. They show the entire possible range of ionospheric parameters before the flash Eq. (1) and at maximum X-ray radiation Eq. (2).

The range of the obtained parameter values is quite wide. Let us try to narrow down the ranges of values of *h*<sup>0</sup> <sup>0</sup> and *β*0. To do this, according to Eq. (3), we calculate the variations of the amplitude and phase of the signals for a time step of 15 seconds. So, for time from *t*<sup>0</sup> to *t*max, we obtained 39 intermediate values of the variations of amplitudes and phases of the signals from the transmitters. Among the family of points (*h*<sup>0</sup> 0 , *β*0), we find those for which there are such points (*h*<sup>t</sup> 0 , *β*t) and for which the variation of amplitude and phase corresponds to that measured for all registered intermediate values:

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

$$\begin{aligned} \left| A\_i \left( h\_t', \boldsymbol{\beta}\_t \right) - A\_i(h\_0', \boldsymbol{\beta}\_0) - d A\_i(t) \right| &< \delta A \\ \left| P\_i \left( h\_t', \boldsymbol{\beta}\_t \right) - P\_i(h\_0', \boldsymbol{\beta}\_0) - d P\_i(t) \right| &< \delta P \end{aligned} \tag{5}$$

where *t*<sup>0</sup> < *t* < *t*max. This family of values is shown in **Figure 8** as black points.

From **Figure 8** it is seen that the initial parameters of the ionosphere lie in a very narrow range. It is approximately 0.6 km in *h*<sup>0</sup> and 0.015 km�<sup>1</sup> in *β*. The dispersion of the ionospheric parameters at the time of maximum flare is approximately the same in *h*<sup>0</sup> and 10 times larger in *β* (about 0.15). This is due to the fact that the flash was sufficiently powerful and at the time of the maximum X-ray flux, the "rigidity" of the upper wall of the waveguide became so large that its further increase had virtually ceased to influence the amplitude-phase characteristics of the received signals.

Further, for the initial parameters of the ionosphere that we calculated (i.e., *h*<sup>0</sup> = 70.7 and *β* = 0.33 km�<sup>1</sup> ), we can use Eq. (5) to continue the calculation of the parameters of the ionosphere for another time: during the decay phase of the X-ray flux, i.e., for time *t* > *t*max, and for the time before flash, i.e., *t* < *t*0. Thus, we can restore the time history of the parameters *h*<sup>0</sup> and *β* for the X-ray flash. These results are shown in **Figure 9b**. This figure clearly shows the advantage of our method. It can be seen how the parameters of the ionosphere changed before the flash from 11:30 to 11:55 UT. The parameter h' changed from 69.5 to 71 km and the *β* parameter from 0.36 to 0.33 km�<sup>1</sup> . This is due to the relaxation of the ionosphere after the previous flare of class X2.2 that occurred at 9:00 UT. The same method was used for

#### **Figure 8.**

flux in two spectral bands according to the Geostationary Operational Environ-

*dAi*ðÞ¼ *<sup>t</sup> <sup>A</sup>*<sup>∗</sup>

*dPi*ðÞ¼ *<sup>t</sup> <sup>P</sup>*<sup>∗</sup>

GQD (*i* = 1) and GBZ (*i* = 2) transmitters shown in **Figure 6**.

The graphical representation of these data is shown in **Figure 7**. Let us denote the parameters of the ionosphere at a time *t*<sup>0</sup> as *h*<sup>0</sup>

*max* and *β*max. In the matrices *Ai*(*h*<sup>0</sup>

Denote the X9.3 flash start time as *t*<sup>0</sup> = 11:52:20 UT and the time of maximum radiation as *t*max = 12:02:14 UT. Let variations of the amplitude and phase *dAi*(*t*) and

*<sup>i</sup>* ð Þ�*<sup>t</sup> <sup>A</sup>*<sup>∗</sup>

*<sup>i</sup>* ðÞ�*<sup>t</sup> <sup>P</sup>*<sup>∗</sup>

On the other hand, the amplitude and phase of the signals of the GQD and GBZ

transmitters depend on the parameters of the ionosphere *h*<sup>0</sup> and *β* on the signal propagation path. Suppose that for the whole GQD-/GBZ-Mikhnevo path, the parameters *h*<sup>0</sup> and *β* are the same. Then, the amplitude and phase values of the signals from the transmitters for all possible pairs of *h*<sup>0</sup> and *β* values were calculated using LWPC code. This code allows to calculate amplitude and phase of the VLF signal for the given path and the values of *h'* and *β* parameters. The calculations were carried out in the range of 50–90 km with 0.035 km increments for *h'* and 0.2– 0.95 km�<sup>1</sup> with 0.001 km�<sup>1</sup> increments for *β*. The ranges of values of *h*<sup>0</sup> and *β* were selected according to [20–22]. Thus, four matrices with the size of 1143 � 751 elements with values of amplitudes and phases of VLF signals versus *h*<sup>0</sup> and *β* parameters were obtained. Let us denote the calculated values of the amplitude and

amplitudes and phases for *t*<sup>0</sup> and *t*max coincides with that measured with given

� *Ai*ð*h* 0

� *Pi*ð*h* 0

where *δ A* = 0.12 dB and *δP* = 0.06 rad are the accuracy of estimation of the

ionosphere at the *t*max lie in the range of 54 < *h*max' < 68 and 0.31 < *β*max < 0.95. The regions of existence of pairs of points (*h*0', *β*0) and (*h*max', *β*max) that satisfy condition Eq. (4) are shown in **Figure 8** by extended gray areas. They show the entire possible range of ionospheric parameters before the flash Eq. (1) and at

The range of the obtained parameter values is quite wide. Let us try to narrow

, *β*0), we find those for which there are such points (*h*<sup>t</sup>

calculate the variations of the amplitude and phase of the signals for a time step of 15 seconds. So, for time from *t*<sup>0</sup> to *t*max, we obtained 39 intermediate values of the variations of amplitudes and phases of the signals from the transmitters. Among the

and for which the variation of amplitude and phase corresponds to that measured

Based on the data [20–22], we assume that the parameters of the ionosphere at *t*<sup>0</sup>

*<sup>i</sup>* ð Þ *t*<sup>0</sup>

(3)

<sup>0</sup> and *β*<sup>0</sup> and at a

*, β*), we can find all pairs

*:* (4)

0 , *β*t)

*<sup>i</sup>* ð Þ *t*<sup>0</sup>

*\** are the measured amplitudes and phases of the signals of the

*, β*), where *i* = 1 for GQD and *i* = 2 for GBZ transmitters.

 <sup>&</sup>lt;*δ<sup>A</sup>*

 <sup>&</sup>lt;*δ<sup>P</sup>*

<sup>0</sup> < 77 km and 0.22 < *β*<sup>0</sup> < 0.35 and the parameters of the

<sup>0</sup> and *β*0. To do this, according to Eq. (3), we

*, β*) и *Pi*(*h*<sup>0</sup>

, *β*0) and (*h'*max, *β*max) for which the difference between the values of

<sup>0</sup>*; β*0Þ � *dAi*ð Þ *t*max

<sup>0</sup>*; β*0Þ � *dPi*ð Þ *t*max

mental Satellite (GOES) data.

*Satellites Missions and Technologies for Geosciences*

*\** and *Pi*

*, β*) and *Pi*(*h*<sup>0</sup>

*Ai h* 0 max*; β*max 

*Pi h* 0 max*; β*max 

 

 

parameters of the ionosphere.

maximum X-ray radiation Eq. (2).

0

for all registered intermediate values:

down the ranges of values of *h*<sup>0</sup>

family of points (*h*<sup>0</sup>

**32**

lie in the range of 68 < *h*<sup>0</sup>

*dPi*(*t*) be determined as

where *Ai*

phase as *Ai*(*h*<sup>0</sup>

time *t*max as *h*<sup>0</sup>

0

of points (*h*<sup>0</sup>

precision:

*The regions of ionospheric parameters h*<sup>0</sup> *and β before the flash Eq. (1) and at maximum X-ray radiation Eq. (2). The regions of existence of pairs of points (h0* 0 *, β0) and (hmax*<sup>0</sup> *, βmax) that satisfy the condition Eq. (4) are extended gray areas.*

upper and lower ionosphere. This result shows that the increase in TEC in both cases was provided by an increase in the electron concentration in the lower

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

**Figure 11** shows the deviations of TEC from the median value over the preceding month of 6 September 2017. This map was constructed from the Madrigal

navigation network by using algorithm Eq. (1) as in the previous section. Comparison of **Figures 4** and **11** shows that the maximum deviation of TEC during a solar flare is about four times less than in the main phase of a strong

*Variations of DEC and TEC during solar flashes X2.2 (a) and X9.3 (b) on 6 September 2017.*

ionosphere.

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

magnetic storm.

**Figure 10.**

**Figure 11.**

**35**

*The ΔTEC value determined by Eq. (1) for 6 September 2017.*

**Figure 9.**

*The time history of the lower ionosphere parameters h*<sup>0</sup> *and β for the X2.2 flash (a) and for the X9.3 flash (b) on 6 September 2017.*

X2.2 flash at 9:00 UT on 6 September 2017. The results of these calculations are shown in the **Figure 9a**.

Knowing the parameters *h*<sup>0</sup> and *β* of the altitude profile of the electron concentration in the D region, it is possible to calculate the electronic content in it. Let us call this parameter DEC. To do this, the value of the electron concentration, that is, defined by Eq. (2), must be integrated in height in according to

$$DEC = \int\_{50}^{h' + \delta h} 1.49 \cdot 10^7 \cdot e^{(\beta - 0.15)\left(z - h'\right)} e^{-0.15h'} dz \tag{6}$$

The lower limit of integration is not very important, since according to Eq. (2) for small z, the electron concentration of Ne is minimal. But there is a problem of choosing the upper limit of integration. This is due to the fact that the according to Eq. (2), the ionosphere shows the exponential growth of the electron concentration with height. At the same time, the electromagnetic wave cannot penetrate into the region of high concentrations and, therefore, does not carry any information about the state of the ionosphere at these heights. So, the VLF radio waves should be reflected in a layer of thickness of the order of wavelength. Therefore, it would be reasonable to carry out the integration up to the height *h*<sup>0</sup> + *δh*, where *δh* is a value comparable to the wavelength.

Now consider the effect of solar X-ray flares in the upper and lower ionosphere comparing the change in TEC of the ionosphere according to GNSS receiver data to electronic content in the D region (let us call this parameter DEC), according to the parameters of VLF radio signals.

**Figure 10** shows DEC and TEC variations caused by flashes X2.2 (a) and X9.3 (b) on 6 September 2017. Here, to calculate DEC the integration of the electron density Ne was carried out up to a height of *h*<sup>0</sup> + 12 km. TEC was calculated from the GPS receiver data installed in the Mikhnevo observatory.

You can see that at the solar flash X2.2, (a) the amplitude of the increase in TEC and DEC was about four times less than during the flash X9.3. So, the change of the amplitude of the perturbations of both the upper and lower ionosphere was directly proportional to the change in the X-ray flux. But the most interesting result of these measurements is the proximity of the electron density perturbations in the

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

upper and lower ionosphere. This result shows that the increase in TEC in both cases was provided by an increase in the electron concentration in the lower ionosphere.

**Figure 11** shows the deviations of TEC from the median value over the preceding month of 6 September 2017. This map was constructed from the Madrigal navigation network by using algorithm Eq. (1) as in the previous section. Comparison of **Figures 4** and **11** shows that the maximum deviation of TEC during a solar flare is about four times less than in the main phase of a strong magnetic storm.

**Figure 10.** *Variations of DEC and TEC during solar flashes X2.2 (a) and X9.3 (b) on 6 September 2017.*

**Figure 11.** *The ΔTEC value determined by Eq. (1) for 6 September 2017.*

X2.2 flash at 9:00 UT on 6 September 2017. The results of these calculations are

*The time history of the lower ionosphere parameters h*<sup>0</sup> *and β for the X2.2 flash (a) and for the X9.3 flash (b)*

<sup>1</sup>*:*<sup>49</sup> � <sup>10</sup><sup>7</sup> � *<sup>e</sup>*

defined by Eq. (2), must be integrated in height in according to

*h* ð 0 þ*δh*

50

GPS receiver data installed in the Mikhnevo observatory.

*DEC* ¼

*Satellites Missions and Technologies for Geosciences*

Knowing the parameters *h*<sup>0</sup> and *β* of the altitude profile of the electron concentration in the D region, it is possible to calculate the electronic content in it. Let us call this parameter DEC. To do this, the value of the electron concentration, that is,

The lower limit of integration is not very important, since according to Eq. (2) for small z, the electron concentration of Ne is minimal. But there is a problem of choosing the upper limit of integration. This is due to the fact that the according to Eq. (2), the ionosphere shows the exponential growth of the electron concentration with height. At the same time, the electromagnetic wave cannot penetrate into the region of high concentrations and, therefore, does not carry any information about the state of the ionosphere at these heights. So, the VLF radio waves should be reflected in a layer of thickness of the order of wavelength. Therefore, it would be reasonable to carry out the integration up to the height *h*<sup>0</sup> + *δh*, where *δh* is a value

Now consider the effect of solar X-ray flares in the upper and lower ionosphere comparing the change in TEC of the ionosphere according to GNSS receiver data to electronic content in the D region (let us call this parameter DEC), according to the

**Figure 10** shows DEC and TEC variations caused by flashes X2.2 (a) and X9.3 (b) on 6 September 2017. Here, to calculate DEC the integration of the electron density Ne was carried out up to a height of *h*<sup>0</sup> + 12 km. TEC was calculated from the

You can see that at the solar flash X2.2, (a) the amplitude of the increase in TEC and DEC was about four times less than during the flash X9.3. So, the change of the amplitude of the perturbations of both the upper and lower ionosphere was directly proportional to the change in the X-ray flux. But the most interesting result of these measurements is the proximity of the electron density perturbations in the

ð Þ *<sup>β</sup>*�0*:*<sup>15</sup> *<sup>z</sup>*�*h*<sup>0</sup> ð Þ*e*

�0*:*15*h*<sup>0</sup>

*dz* (6)

shown in the **Figure 9a**.

**Figure 9.**

*on 6 September 2017.*

comparable to the wavelength.

parameters of VLF radio signals.

**34**

#### **5. Conclusion**

The study of the influence of heliogeophysical phenomena on the conditions of GNSS functioning was carried out under strong magnetic storms and powerful solar X-ray flares. Experiments on these types of events were carried out in one latitudinal zone using one set of data sources. This allows not only to study the disturbances of the upper and lower ionospheres for different geophysical processes but also to compare their nature and magnitude.

a substantially smaller contribution of DEC to TEC in a magnetic storm than in solar

*The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation…*

So, the combined analysis of variations of GNSS signals and signals from VLF radio stations is an effective method for studying the interrelated processes in the

We believe that an important result of our research is to demonstrate that the interpretation of data on the total electronic content of the ionosphere obtained from the data of GNSS receivers should take into account the contribution that can make to the TEC value by the change of electron concentration in the lower ionosphere. On the other hand, the occurrence of significant electron density perturbations in the lower ionosphere should be taken into account in the analysis of factors

The authors are sincerely grateful to Madrigal (http://www.openmadrigal.org/), UK Solar Data Centre (https://www.ukssdc.ac.uk/cgi-bin/digisondes/cost\_database. pl), and Kiel Longwave Monitor (http://www.lf-radio.de/) for providing them with geophysical data and to Johns Hopkins University Applied Physics Laboratory for

X-ray flares.

**Acknowledgements**

**Author details**

**37**

upper and lower ionosphere [23, 24].

*DOI: http://dx.doi.org/10.5772/intechopen.88552*

A17-117112350014-8 and 0146-2015-0017.

affecting the accuracy and reliability of GNSS operation.

This study was conducted within state research targets AAAA-

providing them with DMSP satellite data (http://ssusi.jhuapl.edu/).

Boris Gavrilov\*, Yuriy Poklad and Iliya Ryakhovskiy

provided the original work is properly cited.

Institute of Geosphere Dynamics RAS, Moscow, Russian Federation

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: boris.gavrilov34@gmail.com

The combined analysis of data from GPS receivers, data from the Madrigal network, and data from measurements of VLF radio signals provides a fairly complete picture of the effects caused by X-ray flashes and magnetic super storm in the upper and lower ionospheres.

Both during the solar X-ray flares on 6 September 2017 and during the magnetic storm on 17 March 2015, the significant changes in the electron concentration of the ionosphere were observed. But the amplitude of these changes and most importantly the ratio of the growth of the electron concentration in the D and F regions of the ionosphere differed significantly in these two events.

During the geomagnetic storm on 17 March 2015, TEC disturbances and VLF signal variations correspond to its main phase. TEC increase was about 15–20 TECU (**Figure 2a**). The perturbation of the F layer at solar X-ray flares was much lower and did not exceed 2.5 TECU (**Figure 5a**), so it was approximately eight times less than during the magnetic storm.

Comparison of TEC estimated from the Madrigal GPS network (**Figures 4** and **10**) with the data obtained for individual stations is not correct. However, it can be seen that according to Madrigal, TEC increase during the magnetic storm was significantly stronger. It is also obvious that during the solar flashes, there is a much more uniform distribution of TEC, as it should be in the conditions of illumination by the radiation of the flash throughout Europe.

The analysis of the results of VLF radio signal amplitude variations on the paths from several European and American VLF transmitters to the receiver at Mikhnevo observatory during St. Patrick's Day magnetic storm on 17 March 2015 and solar Xray flares on 6 September 2017 shows the effects of the both events to the lower ionosphere as well.

To estimate the lower ionospheric contribution to the TEC value, we have developed a method for restoring the high-altitude profile of electron concentration in the D region of the ionosphere by using the amplitude and phase characteristics of signals from VLF transmitters on a two-frequency path.

This method is based on known approaches to solving this problem [17–19]. But if in these works the determination of the parameters of the lower ionosphere was carried out by analyzing the contribution to the ionization of X-ray radiation and as the initial (before the flash) parameters of the ionosphere were used parameters determined by models, our technique allows to calculate parameters of the lower ionosphere, in which it was before the disturbance using only the measurement data.

A comparison of TEC value calculated from GPS receiver data with the calculation of the electron content in the D region from the data of the VLF radio signal parameters indicates the possibility of a significant contribution of the lower ionosphere, at least during the powerful X-ray flashes.

We have not been able to calculate DEC for magnetic storm conditions. Changing the parameters of VLF signals was complex and ambiguous. However, the fact that the increase in the amplitudes of VLF signals during the storm was close to the growth of this value during solar X-ray flares and the growth of TEC in a storm significantly exceeded the growth of TEC during solar X-ray flares clearly indicates *The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

a substantially smaller contribution of DEC to TEC in a magnetic storm than in solar X-ray flares.

So, the combined analysis of variations of GNSS signals and signals from VLF radio stations is an effective method for studying the interrelated processes in the upper and lower ionosphere [23, 24].

We believe that an important result of our research is to demonstrate that the interpretation of data on the total electronic content of the ionosphere obtained from the data of GNSS receivers should take into account the contribution that can make to the TEC value by the change of electron concentration in the lower ionosphere. On the other hand, the occurrence of significant electron density perturbations in the lower ionosphere should be taken into account in the analysis of factors affecting the accuracy and reliability of GNSS operation.

#### **Acknowledgements**

**5. Conclusion**

compare their nature and magnitude.

*Satellites Missions and Technologies for Geosciences*

the ionosphere differed significantly in these two events.

of signals from VLF transmitters on a two-frequency path.

sphere, at least during the powerful X-ray flashes.

upper and lower ionospheres.

than during the magnetic storm.

of the flash throughout Europe.

ionosphere as well.

data.

**36**

The study of the influence of heliogeophysical phenomena on the conditions of GNSS functioning was carried out under strong magnetic storms and powerful solar X-ray flares. Experiments on these types of events were carried out in one latitudinal zone using one set of data sources. This allows not only to study the disturbances of the upper and lower ionospheres for different geophysical processes but also to

The combined analysis of data from GPS receivers, data from the Madrigal network, and data from measurements of VLF radio signals provides a fairly complete picture of the effects caused by X-ray flashes and magnetic super storm in the

Both during the solar X-ray flares on 6 September 2017 and during the magnetic storm on 17 March 2015, the significant changes in the electron concentration of the ionosphere were observed. But the amplitude of these changes and most importantly the ratio of the growth of the electron concentration in the D and F regions of

During the geomagnetic storm on 17 March 2015, TEC disturbances and VLF signal variations correspond to its main phase. TEC increase was about 15–20 TECU (**Figure 2a**). The perturbation of the F layer at solar X-ray flares was much lower and did not exceed 2.5 TECU (**Figure 5a**), so it was approximately eight times less

Comparison of TEC estimated from the Madrigal GPS network (**Figures 4** and **10**) with the data obtained for individual stations is not correct. However, it can be seen that according to Madrigal, TEC increase during the magnetic storm was significantly stronger. It is also obvious that during the solar flashes, there is a much more uniform distribution of TEC, as it should be in the conditions of illumination by the radiation

The analysis of the results of VLF radio signal amplitude variations on the paths from several European and American VLF transmitters to the receiver at Mikhnevo observatory during St. Patrick's Day magnetic storm on 17 March 2015 and solar Xray flares on 6 September 2017 shows the effects of the both events to the lower

This method is based on known approaches to solving this problem [17–19]. But if in these works the determination of the parameters of the lower ionosphere was carried out by analyzing the contribution to the ionization of X-ray radiation and as the initial (before the flash) parameters of the ionosphere were used parameters determined by models, our technique allows to calculate parameters of the lower ionosphere, in which it was before the disturbance using only the measurement

A comparison of TEC value calculated from GPS receiver data with the calculation of the electron content in the D region from the data of the VLF radio signal parameters indicates the possibility of a significant contribution of the lower iono-

We have not been able to calculate DEC for magnetic storm conditions. Changing the parameters of VLF signals was complex and ambiguous. However, the fact that the increase in the amplitudes of VLF signals during the storm was close to the growth of this value during solar X-ray flares and the growth of TEC in a storm significantly exceeded the growth of TEC during solar X-ray flares clearly indicates

To estimate the lower ionospheric contribution to the TEC value, we have developed a method for restoring the high-altitude profile of electron concentration in the D region of the ionosphere by using the amplitude and phase characteristics

This study was conducted within state research targets AAAA-A17-117112350014-8 and 0146-2015-0017.

The authors are sincerely grateful to Madrigal (http://www.openmadrigal.org/), UK Solar Data Centre (https://www.ukssdc.ac.uk/cgi-bin/digisondes/cost\_database. pl), and Kiel Longwave Monitor (http://www.lf-radio.de/) for providing them with geophysical data and to Johns Hopkins University Applied Physics Laboratory for providing them with DMSP satellite data (http://ssusi.jhuapl.edu/).

#### **Author details**

Boris Gavrilov\*, Yuriy Poklad and Iliya Ryakhovskiy Institute of Geosphere Dynamics RAS, Moscow, Russian Federation

\*Address all correspondence to: boris.gavrilov34@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[3] Demyanov VV, Zhang X, Lu X. Moderate geomagnetic storm condition, WAAS alerts and real GPS positioning quality. Journal of Atmospheric Science Research. 2019;**2**(1). DOI: 10.30564/jasr. v2i1.343

[4] Ryakhovskiy IA et al. Ionization of the lower ionosphere during the X-ray solar flare on September 6, 2017. In: Proceedings of SPIE 10833, 24th International Symposium on Atmospheric and Ocean Optics; 2018. p. 108339Y. DOI: 10.1117/12.2504402

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[6] Afraimovich EL, Astafyeva EI, Demyanov VV, Gamayunov IF. Midlatitude amplitude scintillation of GPS signals and GPS performance slips. Advances in Space Research. 2009;**43**: 964-972. DOI: 10.1016/j.asr.2008.09.015

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[11] Gavrilov BG, Zetser Yu I, Ryakhovskii IA, Poklad Yu V, Ermak VM. Remote sensing of ELF/VLF radiation induced in experiments on artificial modification of the ionosphere. Geomagnetism and Aeronomy. 2015; **5**(4):450-456. DOI: 10.7868/ S0016794015040045

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[14] Gavrilov BG, Zetser Yu I, Lyakhov AN, Poklad Yu V, Ryakhovskii IA. Spatiotemporal distributions of the electron density in the ionosphere by records of the total electron content and phase of VLF radio *The Influence of the Lower Ionospheric Disturbances on the Operating Conditions of Navigation… DOI: http://dx.doi.org/10.5772/intechopen.88552*

signals. Geomagnetism and Aeronomy. 2017;**57**(4):461-470. DOI: 10.7868/ S001679401704006X

**References**

[1] Afraimovich EL, Boitman ON, Zhovty EI, et al. Dynamics and anisotropy of traveling ionospheric disturbances as deduced from

10.1029/1998RS900004

1144-3. DOI: 10.5772/54568

v2i1.343

[3] Demyanov VV, Zhang X, Lu X. Moderate geomagnetic storm condition, WAAS alerts and real GPS positioning quality. Journal of Atmospheric Science Research. 2019;**2**(1). DOI: 10.30564/jasr.

[4] Ryakhovskiy IA et al. Ionization of the lower ionosphere during the X-ray solar flare on September 6, 2017. In: Proceedings of SPIE 10833, 24th International Symposium on

Atmospheric and Ocean Optics; 2018. p. 108339Y. DOI: 10.1117/12.2504402

[5] Demyanov VV, Yasyukevich YV. GNSS carrier phase noise as a promising means to reconstruct fine structure of the ionosphere. Journal of Aeronautics & Aerospace Engineering. 2018;**7**:60. DOI: 10.4172/2168-9792-C1-22

[6] Afraimovich EL, Astafyeva EI, Demyanov VV, Gamayunov IF. Midlatitude amplitude scintillation of GPS signals and GPS performance slips. Advances in Space Research. 2009;**43**: 964-972. DOI: 10.1016/j.asr.2008.09.015

[7] Afraimovich EL, Ding F, Kiryushkin V, Astafyeva E, Jin S, San'kov V. TEC response to the 2008 Wenchuan earthquake in comparison with other strong earthquakes.

**38**

transionospheric sounding data. Radio Science. 1999;**34**(2):477-487. DOI:

*Satellites Missions and Technologies for Geosciences*

International Journal of Remote Sensing. 2010;**31**(13):3601-3613. DOI: 10.1080/

[8] Hayakawa M, Molchanov OA. Seismo Electromagnetics: Lithosphere-Atmosphere-Ionosphere Coupling. Tokyo: Terra Sci.; 2008. p. 477

[9] Perevalova NP, Sankov VA, Astafyeva EI, Zhupityaeva АS. Threshold magnitude for ionospheric TEC response to earthquakes. Journal of Atmospheric and Solar-Terrestrial

[10] Kumar A, Kumar S. Space weather effects on the low latitude D-region ionosphere during solar minimum. Earth, Planets and Space. 2014. p. 76. DOI: 10.1186/1880-5981-66-76

Ermak VM. Remote sensing of ELF/VLF radiation induced in experiments on artificial modification of the ionosphere. Geomagnetism and Aeronomy. 2015;

Physics. 2014;**108**:77-90

[11] Gavrilov BG, Zetser Yu I, Ryakhovskii IA, Poklad Yu V,

**5**(4):450-456. DOI: 10.7868/ S0016794015040045

202JA017876

[14] Gavrilov BG, Zetser Yu I, Lyakhov AN, Poklad Yu V, Ryakhovskii IA. Spatiotemporal distributions of the electron density in the ionosphere by records of the total electron content and phase of VLF radio

[12] Han F, Cummer SA, Li J, Lu G. Daytime ionospheric D region sharpness derived from VLF radio atmospherics. Journal of Geophysical Research. 2011; **116**(5). DOI: 10.1029/2010JA016299

[13] Maurya AK, Veenadhari B, Singh R, et al. Nighttime D region electron density measurements from ELF-VLF tweek radio atmospherics recorded at low latitudes. Journal of Geophysical Research. 2012;**117**(A11). DOI: 10.1029/

01431161003727747

[2] Demyanov VV, Yu V. Yasyukevich S, Jin S. Effects of solar radio emission and ionospheric irregularities on GPS/ GLONASS performance. In: Jin S, editor. Geodetic Sciences – Observations, Modeling and Applications. InTech; 2013. pp. 177-222. ISBN: 978-953-51[15] Astafyeva E, Zakharenkova IM, Forste M. Ionospheric response to the 2015 St. Patrick's Day storm: A global multi-instrumental overview. Journal of Geophysical Research, Space Physics. 2015;**120**(10):9023-9037. DOI: 10.1002/ 2015JA021629

[16] Borries C, Mahrous AM, Ellahouny NM, Badeke R. Multiple ionospheric perturbations during the Saint Patrick's Day storm 2015 in the European—African sector. Journal of Geophysical Research, Space Physics. 2016;**121**(11):11333-11345. DOI: 10.1002/2016JA023178

[17] Ferguson J, Snyder FP. Computer programs for assessment of long wavelength radio communications. Tech. Doc. 1773, DTIC AD-B144 839. Alexandria, VA: Naval Ocean System Center, Defense Technical Information Center; 1990

[18] Wait JR, Spies KP. Characteristics of the Earth—Ionosphere waveguide for VLF radio waves. Natl. Bur. of Stand. Technical Note; Boulder, Colo; 1964. p. 300

[19] Basak T. Study of the effects on lower ionosphere due to solar phenomena using very low frequency radio wave propagation [thesis]. Department of Physics, University of Calcutta; 2013

[20] Thomson NR. Experimental daytime VLF ionospheric parameters. Journal of Atmospheric and Terrestrial Physics. 1993;**55**(2):173-184. DOI: 10.1016/0021-9169(93)90122

[21] Davis RM, Berry LA. A revised model of the electron density in the lower ionosphere. Tech. Rept TR I I l-77. Defense Commun. Agency Command Control Tech. Center, Washington, DC. NTIS, AD 17883. 1997. p. 58

[22] Gambill B. Normal D-region models for weapon effects code. Defence Nuclear Agency Report. DNA 62715H; 1985

[23] Gavrilov BG, Zetser YI, Lyakhov AN, Poklad YV, Ryakhovskii IA. Correlated disturbances of the upper and lower ionosphere from synchronous measurements of parameters of GNSS signals and VLF radio signals. Cosmic Research. 2019; **57**(1):36-43. DOI: 10.1134/ S0010952519010039

[24] Lyakhov AN, Korsunskaya JA, Poklad YV, et al. The numerical simulation of the 2017 september solar X-flares impact on the midlatitude lower ionosphere. In: Proceedings of SPIE 10833, 24th International Symposium on Atmospheric and Ocean Optics; 108339M; 2018. DOI: 10.1117/ 12.2504292

**Chapter 3**

**Abstract**

irregularities.

**1. Introduction**

seconds [6, 7].

**41**

Real-Time Monitoring of

Ionospheric Irregularities

and TEC Perturbations

*Giorgio Savastano and Michela Ravanelli*

The ionosphere is a part of the upper atmosphere that is a threat to GNSS and satellite telecommunication systems. In this chapter, we will dive into the GNSS real-time monitoring of ionospheric irregularities and TEC perturbations, with a focus on the detection of small- and medium-scale traveling ionospheric disturbances (TIDs) for natural hazard applications. We will describe the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm, which is capable of estimating TEC variations in real time, and it was used to detect tsunamiinduced TIDs. In particular, the analytical and physical implications of applying the VARION algorithm both to GNSS dual-frequency MEO (medium Earth orbit) and GEO (geostationary orbit) satellites will be provided, thus highlighting its relevance for natural hazard early warning systems and real-time monitoring of ionospheric

**Keywords:** VARION algorithm, GNSS, GEO, traveling ionospheric disturbances,

As the title of this book suggests, the Earth's atmosphere represents a threat for GNSS and telecommunications satellites. In particular, the charged component of the upper atmosphere, the ionosphere, is responsible for errors in GNSS positioning that can reach values of tens of meters for single-frequency GNSS receivers [1, 2]. These errors have to be corrected or eliminated in order to make GNSS a valuable

However, the use of GNSS signals is nowdays not only limited to the estimation of the receiver's position, but it has eventually become a key instrument for ionospheric and tropospheric remote sensing studies and for soil features (GNSS reflectometry) [3]. In particular, GNSS can be used to monitor the ionosphere at different time and space scales. On a global scale, GNSS observations are used to generate global ionosphere maps (GIM) by interpolating in both space and time measurements of TEC from stations distributed around the world [4]. On a regional scale, the same signals can be used to detect fast ionospheric disturbances, such as TIDs with periods of minutes to about 1 h [5] and ionospheric scintillation with periods of

tsunami early warning systems, ionospheric irregularities

scientific instrument for geodesy and geodynamics applications.

#### **Chapter 3**

## Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations

*Giorgio Savastano and Michela Ravanelli*

#### **Abstract**

The ionosphere is a part of the upper atmosphere that is a threat to GNSS and satellite telecommunication systems. In this chapter, we will dive into the GNSS real-time monitoring of ionospheric irregularities and TEC perturbations, with a focus on the detection of small- and medium-scale traveling ionospheric disturbances (TIDs) for natural hazard applications. We will describe the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm, which is capable of estimating TEC variations in real time, and it was used to detect tsunamiinduced TIDs. In particular, the analytical and physical implications of applying the VARION algorithm both to GNSS dual-frequency MEO (medium Earth orbit) and GEO (geostationary orbit) satellites will be provided, thus highlighting its relevance for natural hazard early warning systems and real-time monitoring of ionospheric irregularities.

**Keywords:** VARION algorithm, GNSS, GEO, traveling ionospheric disturbances, tsunami early warning systems, ionospheric irregularities

#### **1. Introduction**

As the title of this book suggests, the Earth's atmosphere represents a threat for GNSS and telecommunications satellites. In particular, the charged component of the upper atmosphere, the ionosphere, is responsible for errors in GNSS positioning that can reach values of tens of meters for single-frequency GNSS receivers [1, 2]. These errors have to be corrected or eliminated in order to make GNSS a valuable scientific instrument for geodesy and geodynamics applications.

However, the use of GNSS signals is nowdays not only limited to the estimation of the receiver's position, but it has eventually become a key instrument for ionospheric and tropospheric remote sensing studies and for soil features (GNSS reflectometry) [3]. In particular, GNSS can be used to monitor the ionosphere at different time and space scales. On a global scale, GNSS observations are used to generate global ionosphere maps (GIM) by interpolating in both space and time measurements of TEC from stations distributed around the world [4]. On a regional scale, the same signals can be used to detect fast ionospheric disturbances, such as TIDs with periods of minutes to about 1 h [5] and ionospheric scintillation with periods of seconds [6, 7].

The ionosphere is a very important region of the atmosphere as it carries much valuable information about the Earth's system. In fact, the ionosphere is affected from both ends: (a) from above by space weather, such as geomagnetic storms induced by strong solar events, and (b) from below by events such as extreme terrestrial weather and natural hazards.

is "N-type wave," consisting of leading and trailing shocks connected by smooth linear transition regions. The waveform arises from nonlinear propagation effects: the amplitude of N waves depends on earthquake magnitude, losses of shock fronts, neutral wind speed, etc. This means that also CID is N-shaped and propagates at such velocity [18]. Rayleigh waves travel along the Earth surface at a velocity of 3–4 km/s. They propagate in the form of a train consisting of several oscillations whose typical period is about ten of seconds [20]. As already mentioned, they trigger secondary acoustic waves emitted in the form of the same train, propagating at sound speed. These waves also appear as CID 10–15 min after the earthquake. It is important to highlight that only acoustic waves which have a frequency greater than the cutoff frequency can propagate up to the ionosphere [21]. Such

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

respectively, the specific heat ratio (of the atmosphere) and gravitational acceleration [22, 23]. Thus, waves with a frequency greater than the cutoff one can reach the ionosphere; otherwise their amplitude decreases exponentially with altitude [22], and in this case, the waves are named evanescent. The typical values of cutoff

Gravity waves (GW) form when air parcels are lifted due to particular fluid dynamic and then pulled down by buoyancy in an oscillating manner. This can occur when air passes over mountain chains [24] or when a "mountain," which is read as tsunami wave, moves with a certain velocity. Let us imagine the displacement of a volume of atmospheric air from its equilibrium position; it will then find itself surrounded by air with different density. Buoyant forces will try to bring the volume of air back to the undisturbed position, but these restoring forces will overshoot the target and lead it to oscillate about its neutral buoyancy altitude. It will continue this oscillation about an equilibrium point, generating a gravity wave

Perturbations at the surface that have periods longer than the time needed for the atmosphere to respond under the restoring force of buoyancy will successfully propagate upward. This is known as the Brunt-Vaisala frequency *N* and represents

ð Þ *<sup>g</sup>=<sup>θ</sup>* ð Þ *<sup>d</sup>θ=dz* <sup>p</sup> where g is the gravitational acceleration, *<sup>θ</sup>* is the potential temperature (the temperature that a parcel of air would attain if adiabatically brought to the

Tsunamis have periods longer than this frequency and thus excite atmospheric gravity waves (AGWs) that can propagate upward in the atmosphere and ultimately cause perturbations in the ionospheric electron density. As the kinetic energy is conserved up to an altitude of about 200 km, and air density decreases exponentially with altitude, the AGWs are then strongly amplified in the atmosphere. The ratio of the amplitude of the velocity wave between the ionospheric height and the ground level is about 10<sup>4</sup>–10<sup>5</sup> [25]. This fact was first established in Daniels [26] and was theoretically further developed in Hines [27, 28]. Therefore, it is possible to remotely detect the effects of ocean tsunamis by observing perturbations in the ionosphere. In detail, AGWs which have frequency lower than the Brunt-Vaisala frequency can propagate up through the ionosphere [22]. In the Earth's atmosphere, it depends on the altitude, and it varies from 3.3 to 1.1 mHz (typical value is 2.9 mHz [22]), corresponding to a buoyancy period of 5 min at sea level and about 15 min at 400 km altitude, near the F region peak of the

the maximum frequency for vertically propagating gravity waves. *<sup>N</sup>* <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

<sup>2</sup>*cS* where *cS* is the speed of sound and *γ* and *g* are,

frequency is defined as *<sup>ω</sup><sup>a</sup>* <sup>¼</sup> *<sup>γ</sup><sup>g</sup>*

**2.2 Gravity waves**

frequency fall within the range 2.1–3.3 mHz [15, 22].

that can propagate up through the ionosphere.

ground), and z is the altitude.

ionosphere [19].

**43**

In this chapter, we focus on the real-time monitoring of ionospheric irregularities and TEC perturbations through the application of the VARION algorithm. In Section 2, we review the main mechanisms by which numerous near-ground geophysical (e.g., earthquakes, volcano eruptions, tsunamis) and man-made (e.g., rocket launches) events induce variations in electron density in the ionosphere. In Section 3, we describe the VARION algorithm, which is capable of estimating in real- time changes in the ionospheres' TEC using stand-alone GNSS receivers and can be used for real-time ionosphere remote sensing. In Section 4 we present the main results of the application of the VARION method for two case studies: the 2012 Haida Gwaii tsunami event and a Falcon 9 rocket launch. In Section 5 we present our conclusions.

#### **2. Earth's surface and ionosphere coupling mechanisms**

Acoustic and gravity waves are the two main mechanisms by which energy produced by geophysical events at the Earth's surface can propagate in the atmosphere [8]. The coupling of these atmospheric waves with the ionospheric electron density [9] produces deviations in TEC from the dominant diurnal variation. Traveling ionospheric disturbances (TIDs) are the ionospheric manifestation of these AGWs' induced TEC perturbations. In several applications, such as TID detection, the deviations (also known as fluctuations or perturbations) from the background level are of interest [10, 11]. Other mechanisms by which the ionospheric plasma highly deviates from the dominant diurnal variability are the chemical processes responsible for the ionospheric hole induced by rockets. These processes were described as the interactions between water (*H*2*O*) and hydrogen (*H*2) molecules in the exhaust plume and electrons in the ionosphere, through dissociative recombination.

#### **2.1 Acoustic waves**

Pressure-induced TEC anomalies from earthquakes were widely observed in the last decade, for example, coseismic ionospheric disturbances (CIDs) were documented with the 2003 *MW* 8.3 Tokachi-Oki, Japan and the 2008 *MW* 8.1 Wenchuan, China earthquakes [12] observed at Japanese GEONET sites. CIDs produced by the 2011 *MW* 9.0 Tohoku-Oki, Japan earthquake were reported by several independent research groups [13, 14]. Volcanic eruptions can also excite acoustic waves and induce anomalies in the TEC measurements [15].

When an earthquake occurs, shock acoustic waves (SAWs) are produced in the proximity of the epicenter (within 500 km), and secondary acoustic waves are caused by surface Rayleigh waves propagating far from the epicenter. These pressure waves, upon reaching the ionosphere, will locally affect electron density through particle collisions between the neutral atmosphere and the ionospheric plasma [16]. SAWs, governed primarily by longitudinal compression, can propagate through the atmosphere at the sound speed which varies from several hundred m/s near sea level to 1 km/s at 400 km altitude [17]. At the height of the ionosphere F layer, it is about 800–1000 m/s [18], so it takes between 10 and 15 min to reach the ionosphere and cause the abovementioned disturbance (CID) [19]. Their waveform

#### *Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

is "N-type wave," consisting of leading and trailing shocks connected by smooth linear transition regions. The waveform arises from nonlinear propagation effects: the amplitude of N waves depends on earthquake magnitude, losses of shock fronts, neutral wind speed, etc. This means that also CID is N-shaped and propagates at such velocity [18]. Rayleigh waves travel along the Earth surface at a velocity of 3–4 km/s. They propagate in the form of a train consisting of several oscillations whose typical period is about ten of seconds [20]. As already mentioned, they trigger secondary acoustic waves emitted in the form of the same train, propagating at sound speed. These waves also appear as CID 10–15 min after the earthquake.

It is important to highlight that only acoustic waves which have a frequency greater than the cutoff frequency can propagate up to the ionosphere [21]. Such frequency is defined as *<sup>ω</sup><sup>a</sup>* <sup>¼</sup> *<sup>γ</sup><sup>g</sup>* <sup>2</sup>*cS* where *cS* is the speed of sound and *γ* and *g* are, respectively, the specific heat ratio (of the atmosphere) and gravitational acceleration [22, 23]. Thus, waves with a frequency greater than the cutoff one can reach the ionosphere; otherwise their amplitude decreases exponentially with altitude [22], and in this case, the waves are named evanescent. The typical values of cutoff frequency fall within the range 2.1–3.3 mHz [15, 22].

#### **2.2 Gravity waves**

The ionosphere is a very important region of the atmosphere as it carries much valuable information about the Earth's system. In fact, the ionosphere is affected from both ends: (a) from above by space weather, such as geomagnetic storms induced by strong solar events, and (b) from below by events such as extreme

In this chapter, we focus on the real-time monitoring of ionospheric irregularities and TEC perturbations through the application of the VARION algorithm. In Section 2, we review the main mechanisms by which numerous near-ground geophysical (e.g., earthquakes, volcano eruptions, tsunamis) and man-made (e.g., rocket launches) events induce variations in electron density in the ionosphere. In Section 3, we describe the VARION algorithm, which is capable of estimating in real- time changes in the ionospheres' TEC using stand-alone GNSS receivers and can be used for real-time ionosphere remote sensing. In Section 4 we present the main results of the application of the VARION method for two case studies: the 2012 Haida Gwaii tsunami event and a Falcon 9 rocket launch. In Section 5 we

**2. Earth's surface and ionosphere coupling mechanisms**

Acoustic and gravity waves are the two main mechanisms by which energy produced by geophysical events at the Earth's surface can propagate in the atmosphere [8]. The coupling of these atmospheric waves with the ionospheric electron density [9] produces deviations in TEC from the dominant diurnal variation. Traveling ionospheric disturbances (TIDs) are the ionospheric manifestation of these AGWs' induced TEC perturbations. In several applications, such as TID detection, the deviations (also known as fluctuations or perturbations) from the background level are of interest [10, 11]. Other mechanisms by which the ionospheric plasma highly deviates from the dominant diurnal variability are the chemical processes responsible for the ionospheric hole induced by rockets. These processes were described as the interactions between water (*H*2*O*) and hydrogen (*H*2) molecules in the exhaust plume and electrons in the ionosphere, through dissociative

Pressure-induced TEC anomalies from earthquakes were widely observed in the

When an earthquake occurs, shock acoustic waves (SAWs) are produced in the proximity of the epicenter (within 500 km), and secondary acoustic waves are caused by surface Rayleigh waves propagating far from the epicenter. These pressure waves, upon reaching the ionosphere, will locally affect electron density through particle collisions between the neutral atmosphere and the ionospheric plasma [16]. SAWs, governed primarily by longitudinal compression, can propagate through the atmosphere at the sound speed which varies from several hundred m/s near sea level to 1 km/s at 400 km altitude [17]. At the height of the ionosphere F layer, it is about 800–1000 m/s [18], so it takes between 10 and 15 min to reach the ionosphere and cause the abovementioned disturbance (CID) [19]. Their waveform

last decade, for example, coseismic ionospheric disturbances (CIDs) were documented with the 2003 *MW* 8.3 Tokachi-Oki, Japan and the 2008 *MW* 8.1 Wenchuan, China earthquakes [12] observed at Japanese GEONET sites. CIDs produced by the 2011 *MW* 9.0 Tohoku-Oki, Japan earthquake were reported by several independent research groups [13, 14]. Volcanic eruptions can also excite

acoustic waves and induce anomalies in the TEC measurements [15].

terrestrial weather and natural hazards.

*Satellites Missions and Technologies for Geosciences*

present our conclusions.

recombination.

**42**

**2.1 Acoustic waves**

Gravity waves (GW) form when air parcels are lifted due to particular fluid dynamic and then pulled down by buoyancy in an oscillating manner. This can occur when air passes over mountain chains [24] or when a "mountain," which is read as tsunami wave, moves with a certain velocity. Let us imagine the displacement of a volume of atmospheric air from its equilibrium position; it will then find itself surrounded by air with different density. Buoyant forces will try to bring the volume of air back to the undisturbed position, but these restoring forces will overshoot the target and lead it to oscillate about its neutral buoyancy altitude. It will continue this oscillation about an equilibrium point, generating a gravity wave that can propagate up through the ionosphere.

Perturbations at the surface that have periods longer than the time needed for the atmosphere to respond under the restoring force of buoyancy will successfully propagate upward. This is known as the Brunt-Vaisala frequency *N* and represents the maximum frequency for vertically propagating gravity waves. *<sup>N</sup>* <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð Þ *<sup>g</sup>=<sup>θ</sup>* ð Þ *<sup>d</sup>θ=dz* <sup>p</sup> where g is the gravitational acceleration, *<sup>θ</sup>* is the potential tempera-

ture (the temperature that a parcel of air would attain if adiabatically brought to the ground), and z is the altitude.

Tsunamis have periods longer than this frequency and thus excite atmospheric gravity waves (AGWs) that can propagate upward in the atmosphere and ultimately cause perturbations in the ionospheric electron density. As the kinetic energy is conserved up to an altitude of about 200 km, and air density decreases exponentially with altitude, the AGWs are then strongly amplified in the atmosphere. The ratio of the amplitude of the velocity wave between the ionospheric height and the ground level is about 10<sup>4</sup>–10<sup>5</sup> [25]. This fact was first established in Daniels [26] and was theoretically further developed in Hines [27, 28]. Therefore, it is possible to remotely detect the effects of ocean tsunamis by observing perturbations in the ionosphere. In detail, AGWs which have frequency lower than the Brunt-Vaisala frequency can propagate up through the ionosphere [22]. In the Earth's atmosphere, it depends on the altitude, and it varies from 3.3 to 1.1 mHz (typical value is 2.9 mHz [22]), corresponding to a buoyancy period of 5 min at sea level and about 15 min at 400 km altitude, near the F region peak of the ionosphere [19].

TIDs can be detected using different observing methods, including ionosondes [29]; ground-based GPS total electron content (TEC) [17, 30]; dual-frequency, space-based altimeters [31]; incoherent backscatter radar (ISR) [32]; and spacebased GNSS-RO measurements [33]. Perturbations in the neutral atmosphere after the 2011 Tohoku-Oki tsunami event have also been detected using accelerometers and thruster data from the GOCE mission [34]. Several other causes are responsible for TIDs, such as intense or large-scale tropospheric weather [35], geomagnetic and auroral activity [36, 37], and earthquakes [38–40]. For this reason, the relationship between detected TIDs and those that are induced by a tsunami has to be proven, for example, by verifying that the horizontal speed, direction, and spectral bandwidth of the TIDs match that of the ocean tsunami [5].

**2.4 Dissociative recombination**

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

**3. VARION approach**

**3.1 VARION-GNSS**

phase ambiguity and the IFB:

**45**

*LGF*ð Þ� *<sup>t</sup>* <sup>þ</sup> <sup>1</sup> *LGF*ðÞ¼ *<sup>t</sup> <sup>f</sup>*

Several studies were carried out to analyze the ionospheric responses to rocket launches. The first detection of a localized reduction of ionization due to the interaction between the ionosphere and the exhaust plume of the Vanguard II rocket was reported in [44]. More than a decade after that observation, a sudden decrease in total electron content (TEC) was observed after the 1973 NASA's Skylab launch [45] by measuring the Faraday rotation of radio signals from a geostationary satellite. This study [45] was reported a dramatic bite-out of more than 50% of the TEC magnitude having a duration of nearly 4 h and spatial extent of about 1000 km radius. The chemical processes responsible for the ionospheric hole were described as the interactions between water (*H*2*O*) and hydrogen (*H*2) molecules in the exhaust plume and electrons in the ionosphere, through dissociative recombination. At the level of concentration at which the reactants (*H*2*O* and *H*2) were added to the ionosphere by the rocket's engines, the loss process became 100 times more efficient than the normal loss mechanism in the ionosphere (e.g., *N*2). Localized plasma density depletions during rocket launches were detected also using other measurement techniques, such as ground-based incoherent scatter radar and digisonde [46, 47] and continuous Global Positioning System (GPS) receivers [48, 49].

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

Multiple algorithms were developed to estimate useful ionospheric parameters

The VARION approach is based on single time differences of geometry-free combinations of GNSS carrier-phase measurements (*L*<sup>1</sup> � *L*2), using a stand-alone GNSS receiver and standard GNSS broadcast orbits available in real time. The unknown carrier-phase ambiguity can be considered constant between two consecutive epochs as long as no cycle slips occur. In the case that a cycle slip does occur, then the phase jump can be removed in real time as it represents an outlier in the time series analysis. The receiver and the satellite IFBs in the carrier-phase ionospheric observable are also assumed as constant for a given period [54]. Multipath terms cannot be considered constant between epochs for sampling rates greater than 1 second [55]. However, these terms can be mitigated by applying an elevation cutoff mask of 20 degrees or higher and will be ignored in the following equations for the sake of simplicity. For these reasons, we can write the geometry-free time single-difference observation equation [5], with no need of estimate in real time the

where the term *LGF* refers to the geometry-free combination and *f* <sup>1</sup> and *f* <sup>2</sup> are the two frequencies in L-band transmitted by any GNSS satellites. Taking into

*I <sup>S</sup>*

<sup>1</sup>*<sup>R</sup>* ð Þ� *<sup>t</sup>* <sup>þ</sup> <sup>1</sup> *<sup>I</sup> <sup>S</sup>*

<sup>1</sup>*<sup>R</sup>* ð Þ*<sup>t</sup>* (1)

from GNSS signals, such as absolute TEC measurements [4, 50], relative TEC [51, 52], and TEC variations [5]. In this section, we review the main concepts of the VARION approach, which was first presented in [5] for GNSS satellites (Section 3.1) and subsequently expanded to geostationary satellites in [53] (Section 3.2).

The vertical propagation speed of an atmospheric gravity wave at these periods is 40–50 *m*/*s* [41], so these perturbations should first be observed about 2 *h* after the onset of the tsunami. The TEC anomalies can be identified by their horizontal propagation speed, which is much slower (200–300 *m*/*s*) than that of the acoustic TID or Rayleigh-wave-induced anomalies and follows the propagation speed of the tsunami itself, which is, much like the Rayleigh waves in the acoustic case, a moving source of gravity waves. However, following the 2011 *MW* 9.0 Tohoku-Oki, Japan event, which provided dense near-field TEC observations, it was noted that the onset of the gravity-wave-induced TEC anomalies was shorter, at about 30 *min* after the start of the earthquake, and not the 1.5–2 *h* predicted by previous theoretical computations [17]. This is explained as evidence that it might not be necessary for the gravity wave to reach the F layer peak (around 300 km altitude) for the TEC disturbance to be measurable. Rather, disturbances at lower altitudes within the E layer and the lower portion of the F layer might be substantial enough to be seen in the TEC observations. This is supported by previous modeling results that showed significant TEC perturbations over a broad area around the F layer peak [14]. Through comparisons with tsunami simulations of the event, it was convincingly demonstrated that the tsunami itself must be the source of the observed gravity waves [17]. In light of these observations, ionospheric soundings may be used to monitor tsunamis and issue warnings in advance of their arrival at the coast [3, 5].

#### **2.3 Traveling ionospheric disturbances**

Disturbances in the ionosphere naturally occur at many different scales. On a planetary scale, Rossby waves result from latitudinal variations in the strength of the Coriolis effect and have wavelengths of 1000s of km, while, at smaller scales, acoustic gravity waves induced by natural hazards have typical wavelengths in the range of 10-300 km. Based on their phase velocity, wave period, and horizontal wavelength, TIDs are often classified into medium-scale TID (MSTID) and largescale TID (LSTID). Some guidelines on the properties of these two groups are summarized in **Table 1**, which was created from [42, 43].

In this chapter, we mainly take into account MSTIDs, as they are the one typically generated by tsunami waves and other natural hazards.


**Table 1.**

*TID classification based on phase velocity, wave period, and horizontal wavelength.*

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

#### **2.4 Dissociative recombination**

TIDs can be detected using different observing methods, including ionosondes [29]; ground-based GPS total electron content (TEC) [17, 30]; dual-frequency, space-based altimeters [31]; incoherent backscatter radar (ISR) [32]; and spacebased GNSS-RO measurements [33]. Perturbations in the neutral atmosphere after the 2011 Tohoku-Oki tsunami event have also been detected using accelerometers and thruster data from the GOCE mission [34]. Several other causes are responsible for TIDs, such as intense or large-scale tropospheric weather [35], geomagnetic and auroral activity [36, 37], and earthquakes [38–40]. For this reason, the relationship between detected TIDs and those that are induced by a tsunami has to be proven, for example, by verifying that the horizontal speed, direction, and spectral band-

The vertical propagation speed of an atmospheric gravity wave at these periods is 40–50 *m*/*s* [41], so these perturbations should first be observed about 2 *h* after the onset of the tsunami. The TEC anomalies can be identified by their horizontal propagation speed, which is much slower (200–300 *m*/*s*) than that of the acoustic TID or Rayleigh-wave-induced anomalies and follows the propagation speed of the tsunami itself, which is, much like the Rayleigh waves in the acoustic case, a moving source of gravity waves. However, following the 2011 *MW* 9.0 Tohoku-Oki, Japan event, which provided dense near-field TEC observations, it was noted that the onset of the gravity-wave-induced TEC anomalies was shorter, at about 30 *min* after the start of the earthquake, and not the 1.5–2 *h* predicted by previous theoretical computations [17]. This is explained as evidence that it might not be necessary for the gravity wave to reach the F layer peak (around 300 km altitude) for the TEC disturbance to be measurable. Rather, disturbances at lower altitudes within the E layer and the lower portion of the F layer might be substantial enough to be seen in the TEC observations. This is supported by previous modeling results that showed significant TEC perturbations over a broad area around the F layer peak [14]. Through comparisons with tsunami simulations of the event, it was convincingly demonstrated that the tsunami itself must be the source of the observed gravity waves [17]. In light of these observations, ionospheric soundings may be used to monitor tsunamis and issue warnings in advance of their arrival at the coast [3, 5].

Disturbances in the ionosphere naturally occur at many different scales. On a planetary scale, Rossby waves result from latitudinal variations in the strength of the Coriolis effect and have wavelengths of 1000s of km, while, at smaller scales, acoustic gravity waves induced by natural hazards have typical wavelengths in the range of 10-300 km. Based on their phase velocity, wave period, and horizontal wavelength, TIDs are often classified into medium-scale TID (MSTID) and largescale TID (LSTID). Some guidelines on the properties of these two groups are

In this chapter, we mainly take into account MSTIDs, as they are the one

Large scale 30–300 400–1000 1000–3000 Medium scale 10–60 50–300 10–500

**Period [min] Phase velocity [m/s] Horizontal wavelength [km]**

width of the TIDs match that of the ocean tsunami [5].

*Satellites Missions and Technologies for Geosciences*

**2.3 Traveling ionospheric disturbances**

**Table 1.**

**44**

summarized in **Table 1**, which was created from [42, 43].

typically generated by tsunami waves and other natural hazards.

*TID classification based on phase velocity, wave period, and horizontal wavelength.*

Several studies were carried out to analyze the ionospheric responses to rocket launches. The first detection of a localized reduction of ionization due to the interaction between the ionosphere and the exhaust plume of the Vanguard II rocket was reported in [44]. More than a decade after that observation, a sudden decrease in total electron content (TEC) was observed after the 1973 NASA's Skylab launch [45] by measuring the Faraday rotation of radio signals from a geostationary satellite. This study [45] was reported a dramatic bite-out of more than 50% of the TEC magnitude having a duration of nearly 4 h and spatial extent of about 1000 km radius. The chemical processes responsible for the ionospheric hole were described as the interactions between water (*H*2*O*) and hydrogen (*H*2) molecules in the exhaust plume and electrons in the ionosphere, through dissociative recombination. At the level of concentration at which the reactants (*H*2*O* and *H*2) were added to the ionosphere by the rocket's engines, the loss process became 100 times more efficient than the normal loss mechanism in the ionosphere (e.g., *N*2). Localized plasma density depletions during rocket launches were detected also using other measurement techniques, such as ground-based incoherent scatter radar and digisonde [46, 47] and continuous Global Positioning System (GPS) receivers [48, 49].

#### **3. VARION approach**

Multiple algorithms were developed to estimate useful ionospheric parameters from GNSS signals, such as absolute TEC measurements [4, 50], relative TEC [51, 52], and TEC variations [5]. In this section, we review the main concepts of the VARION approach, which was first presented in [5] for GNSS satellites (Section 3.1) and subsequently expanded to geostationary satellites in [53] (Section 3.2).

#### **3.1 VARION-GNSS**

The VARION approach is based on single time differences of geometry-free combinations of GNSS carrier-phase measurements (*L*<sup>1</sup> � *L*2), using a stand-alone GNSS receiver and standard GNSS broadcast orbits available in real time. The unknown carrier-phase ambiguity can be considered constant between two consecutive epochs as long as no cycle slips occur. In the case that a cycle slip does occur, then the phase jump can be removed in real time as it represents an outlier in the time series analysis. The receiver and the satellite IFBs in the carrier-phase ionospheric observable are also assumed as constant for a given period [54]. Multipath terms cannot be considered constant between epochs for sampling rates greater than 1 second [55]. However, these terms can be mitigated by applying an elevation cutoff mask of 20 degrees or higher and will be ignored in the following equations for the sake of simplicity. For these reasons, we can write the geometry-free time single-difference observation equation [5], with no need of estimate in real time the phase ambiguity and the IFB:

$$L\_{GF}(t+1) - L\_{GF}(t) = \frac{f\_1^2 - f\_2^2}{f\_2^2} \left[ I\_{1\mathbb{R}}{}^S(t+1) - I\_{1\mathbb{R}}{}^S(t) \right] \tag{1}$$

where the term *LGF* refers to the geometry-free combination and *f* <sup>1</sup> and *f* <sup>2</sup> are the two frequencies in L-band transmitted by any GNSS satellites. Taking into

account the ionospheric refraction along the geometric range, we compute the sTEC variations between two consecutive epochs:

$$\text{\ $STEC}(t+1, t) = \frac{\int \,\_1^2 f\_2 \, ^2 \text{\$ }\_{\text{GF}}[L\_{\text{GF}}(t+1) - L\_{\text{GF}}(t)]}{A \left(f\_1 \, ^2 - f\_2 \, ^2 \right)} [L\_{\text{GF}}(t+1) - L\_{\text{GF}}(t)] \tag{2}$$

Eq. (3), increases for lower elevation angles, when the length of the signal path inside the ionosphere is longer, leading to larger *δsTEC* values. This explains the current limitation of GNSS ionospheric-based early warning algorithms for low elevation angles. In particular, it is a common practice to apply a cutoff elevation angle for GNSS ionospheric remote sensing studies which is much higher (20 degrees or higher) than the one normally used for GNSS positioning applications

After identifying and removing cycle slips from *δsTEC* time series, we integrate Eq. (3) over time in order to reconstruct the final Δ*sTEC* perturbation term. The VARION approach overcomes the problem of estimating the phase initial ambiguity and the satellite inter-frequency biases (IFBs), which can be assumed constant for

A GEO satellite experiences libration only (i.e., drifting back and forth between

þ *V* ! !

*ipp* is negligible. For this

*pla* � ∇*sTEC t*ð Þ , *s* , (4)

two stable points), so that it can be considered motionless relative to an ECEF

*∂t*

which can be considered the new VARION-GEO observable. Eq. (4) formally reveals the fundamental property of GEO satellites: independence of the estimated *δsTEC* value on the motion of the IPP. Since GEO observations have a constant elevation angle, we can assume a constant level of observational noise throughout the entire period of observation. Furthermore, GEO observations are less prone to trends induced at low elevation angles, when the length of the signal path inside the ionosphere is longer, leading to larger *δsTEC* values. The other important advantage of GEO satellites is the fact that they provide long-term continuous time series over

In this section, we will give an outlook on the main results achieved through the

Using the VARION algorithm, we compute TEC variations induced by the 2012 Haida Gwaii tsunami event at 56 GPS receivers from Plate Boundary Observatory (PBO) in Hawaiian Islands. All the GPS permanent stations are located in Big Island (see **Figure 1**) and acquired observations at 15 and 30 second rate. In order to validate the methodology, results were, hence, compared with the real-time

VARION approach. In particular, we will show the main results from [5] for tsunami-generated TID detection (Section 4.1) and from [53] for ionospheric plasma depletion analysis (Section 4.2). For more details on these test cases and on the related data processing performed with VARION, please refer to the cited

a given period [5], thus being ideal for real-time applications.

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

reference frame, and as a result the IPP's velocity vector *V*

*dt* <sup>¼</sup> *<sup>∂</sup> sTEC t*ð Þ , *<sup>s</sup>*

*d sTEC t*ð Þ , *s*

**4.1 Haida Gwaii tsunami-induced TIDs**

(5 degrees or lower).

**3.2 VARION-geo**

a fixed location.

**4. Main results**

papers.

**47**

*4.1.1 Dataset*

reason, Eq. (3) becomes:

where *<sup>A</sup>* <sup>¼</sup> <sup>40</sup>*:*<sup>3</sup> � <sup>10</sup><sup>16</sup> ½ � *<sup>m</sup>* ½ � *Hz* <sup>2</sup> ½ � *TECU* �<sup>1</sup> is the standard conversion factor linking TEC [TECU] to ionospheric delay in metric unit [meters]. The discrete derivative of *sTEC* over time can be simply computed dividing *δsTEC* by the interval between epochs *t* and (*t* + 1). *sTEC* is an integrated quantity representing the total number of electrons included in a column with a cross-sectional area of 1 *m*2, counted along the signal path *s* between the satellite *S* and the receiver *R*. The sTEC observations are modeled by collapsing them to the ionospheric pierce point (IPP) between the satellite-receiver line-of-sight and the single-shell layer located above the height of F2 peak, where the electron density is assumed to be maximum. The IPP position can be computed in real time using standard GNSS broadcast orbit parameters [5], after having chosen the height of the F2 peak.

In this work, single-shell ionospheric layer approximation was applied to explain the physical meaning of the *δsTEC* values provided by VARION and to explicitly show the effect of the IPP motion in the VARION observation equation. This singleshell ionospheric approximation means that the ionospheric sTEC is assigned to an IPP point which renders a 2D picture without vertical dependence of any parameter. In this 2D representation of the ionosphere, the variation *δsTEC* in the interval *δt* is equivalent to a total derivative over time where the observational point (IPP) moves independently of the motion of the medium (ionospheric plasma). The total derivative encompasses both the variation in time in a certain fixed position (sTEC partial time derivative) and the variation in time due to the sTEC horizontal spatial variation and to the horizontal motion of the IPP relative to the horizontal plasma flow (the relative IPP velocity times the sTEC 2D space gradient on the ionospheric layer); therefore the VARION-MEO (hereafter called VARION-GNSS):

$$\frac{d\,s\,TEC(t,s)}{dt} = \frac{\partial\,s\,TEC(t,s)}{\partial t} + \left(\vec{V}\_{pla} - \vec{V}\_{ipp}\right) \cdot \nabla\,t\,TEC(t,s),\tag{3}$$

where *V* ! *pla* and *V* ! *ipp* are the horizontal plasma and IPP vector velocity field in an Earth-centered Earth-fixed (ECEF) reference frame (WGS84, in our case, since we are using broadcast orbits), respectively, and ∇*sTEC t*ð Þ , *s* is the horizontal spatial gradient of sTEC. It is clear that the convective derivative term accounts for IPP motion and plasma motion (*V* ! *pla* � *V* ! *ipp*). It is important to underline that in a full 3D representation of the ionosphere, *V* ! *pla*, *V* ! *ipp*, and ∇*sTEC t*ð Þ , *s* are altitudedependent terms; in our 2D single-shell ionospheric layer approximation, all these terms are referred to a 300 km height. However, for the purpose of this paper, Eq. (3) already shows that the ionospheric remote sensing based on GNSS observations acquired from MEO satellites depends on the time-dependent position of the IPPs. It is crucial to underline that the *V* ! *ipp* magnitude is not constant during the period of observation, but it increases for lower elevation angles [56]. In [53] it was shown that the *V* ! *ipp* magnitudes range 40–120 m/s for elevation angles 30–90 degrees, meaning that these IPPs have a velocity of the same order of magnitude of most of the ionospheric perturbations induced by natural hazards (e.g., tsunamiinduced TIDs). Also, the background noise, and long period trends of *δsTEC* in

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

Eq. (3), increases for lower elevation angles, when the length of the signal path inside the ionosphere is longer, leading to larger *δsTEC* values. This explains the current limitation of GNSS ionospheric-based early warning algorithms for low elevation angles. In particular, it is a common practice to apply a cutoff elevation angle for GNSS ionospheric remote sensing studies which is much higher (20 degrees or higher) than the one normally used for GNSS positioning applications (5 degrees or lower).

After identifying and removing cycle slips from *δsTEC* time series, we integrate Eq. (3) over time in order to reconstruct the final Δ*sTEC* perturbation term. The VARION approach overcomes the problem of estimating the phase initial ambiguity and the satellite inter-frequency biases (IFBs), which can be assumed constant for a given period [5], thus being ideal for real-time applications.

#### **3.2 VARION-geo**

account the ionospheric refraction along the geometric range, we compute the sTEC

<sup>2</sup> � *<sup>f</sup>* <sup>2</sup>

In this work, single-shell ionospheric layer approximation was applied to explain the physical meaning of the *δsTEC* values provided by VARION and to explicitly show the effect of the IPP motion in the VARION observation equation. This singleshell ionospheric approximation means that the ionospheric sTEC is assigned to an IPP point which renders a 2D picture without vertical dependence of any parameter. In this 2D representation of the ionosphere, the variation *δsTEC* in the interval *δt* is equivalent to a total derivative over time where the observational point (IPP) moves independently of the motion of the medium (ionospheric plasma). The total derivative encompasses both the variation in time in a certain fixed position (sTEC partial time derivative) and the variation in time due to the sTEC horizontal spatial variation and to the horizontal motion of the IPP relative to the horizontal plasma flow (the relative IPP velocity times the sTEC 2D space gradient on the ionospheric

<sup>2</sup> ½ � *LGF*ð Þ� *<sup>t</sup>* <sup>þ</sup> <sup>1</sup> *LGF*ð Þ*<sup>t</sup>* (2)

½ � *TECU* �<sup>1</sup> is the standard conversion factor

2 *f* 2 2

linking TEC [TECU] to ionospheric delay in metric unit [meters]. The discrete derivative of *sTEC* over time can be simply computed dividing *δsTEC* by the interval between epochs *t* and (*t* + 1). *sTEC* is an integrated quantity representing the total number of electrons included in a column with a cross-sectional area of 1 *m*2, counted along the signal path *s* between the satellite *S* and the receiver *R*. The sTEC observations are modeled by collapsing them to the ionospheric pierce point (IPP) between the satellite-receiver line-of-sight and the single-shell layer located above the height of F2 peak, where the electron density is assumed to be maximum. The IPP position can be computed in real time using standard GNSS broadcast orbit

*A f* <sup>1</sup>

variations between two consecutive epochs:

*Satellites Missions and Technologies for Geosciences*

where *<sup>A</sup>* <sup>¼</sup> <sup>40</sup>*:*<sup>3</sup> � <sup>10</sup><sup>16</sup> ½ � *<sup>m</sup>* ½ � *Hz* <sup>2</sup>

*d sTEC t*ð Þ , *s*

3D representation of the ionosphere, *V*

IPPs. It is crucial to underline that the *V*

!

*pla* and *V* !

motion and plasma motion (*V*

where *V* !

shown that the *V*

**46**

*<sup>δ</sup>sTEC t*ð Þ¼ <sup>þ</sup> 1, *<sup>t</sup> <sup>f</sup>* <sup>1</sup>

parameters [5], after having chosen the height of the F2 peak.

layer); therefore the VARION-MEO (hereafter called VARION-GNSS):

þ *V* ! *pla* � *V* ! *ipp* 

Earth-centered Earth-fixed (ECEF) reference frame (WGS84, in our case, since we are using broadcast orbits), respectively, and ∇*sTEC t*ð Þ , *s* is the horizontal spatial gradient of sTEC. It is clear that the convective derivative term accounts for IPP

> ! *pla*, *V* !

dependent terms; in our 2D single-shell ionospheric layer approximation, all these terms are referred to a 300 km height. However, for the purpose of this paper, Eq. (3) already shows that the ionospheric remote sensing based on GNSS observations acquired from MEO satellites depends on the time-dependent position of the

!

period of observation, but it increases for lower elevation angles [56]. In [53] it was

degrees, meaning that these IPPs have a velocity of the same order of magnitude of most of the ionospheric perturbations induced by natural hazards (e.g., tsunamiinduced TIDs). Also, the background noise, and long period trends of *δsTEC* in

*ipp* magnitudes range 40–120 m/s for elevation angles 30–90

*ipp* are the horizontal plasma and IPP vector velocity field in an

*ipp*). It is important to underline that in a full

*ipp*, and ∇*sTEC t*ð Þ , *s* are altitude-

*ipp* magnitude is not constant during the

� ∇*sTEC t*ð Þ , *s* , (3)

*∂t*

*dt* <sup>¼</sup> *<sup>∂</sup> sTEC t*ð Þ , *<sup>s</sup>*

! *pla* � *V* !

A GEO satellite experiences libration only (i.e., drifting back and forth between two stable points), so that it can be considered motionless relative to an ECEF reference frame, and as a result the IPP's velocity vector *V* ! *ipp* is negligible. For this reason, Eq. (3) becomes:

$$\frac{d\,s\,TEC(t,s)}{dt} = \frac{\partial\,s\,TEC(t,s)}{\partial t} + \vec{V}\_{pla} \cdot \nabla s\,TEC(t,s),\tag{4}$$

which can be considered the new VARION-GEO observable. Eq. (4) formally reveals the fundamental property of GEO satellites: independence of the estimated *δsTEC* value on the motion of the IPP. Since GEO observations have a constant elevation angle, we can assume a constant level of observational noise throughout the entire period of observation. Furthermore, GEO observations are less prone to trends induced at low elevation angles, when the length of the signal path inside the ionosphere is longer, leading to larger *δsTEC* values. The other important advantage of GEO satellites is the fact that they provide long-term continuous time series over a fixed location.

#### **4. Main results**

In this section, we will give an outlook on the main results achieved through the VARION approach. In particular, we will show the main results from [5] for tsunami-generated TID detection (Section 4.1) and from [53] for ionospheric plasma depletion analysis (Section 4.2). For more details on these test cases and on the related data processing performed with VARION, please refer to the cited papers.

#### **4.1 Haida Gwaii tsunami-induced TIDs**

#### *4.1.1 Dataset*

Using the VARION algorithm, we compute TEC variations induced by the 2012 Haida Gwaii tsunami event at 56 GPS receivers from Plate Boundary Observatory (PBO) in Hawaiian Islands. All the GPS permanent stations are located in Big Island (see **Figure 1**) and acquired observations at 15 and 30 second rate. In order to validate the methodology, results were, hence, compared with the real-time

**Figure 1.** *Map indicating the epicenter of the 10/27/2012 Canadian earthquake (left panel) and zoomed-in image of the Hawaii big island, where the 56 used GPS stations are located. Figure adapted from Savastano et al. [5].*

tsunami Method of Splitting Tsunami (MOST) model produced by the NOAA Center for Tsunami Research [57, 58].

#### *4.1.2 Results and discussion*

VARION processing outcame a TEC perturbation with amplitudes of up to 0.25 TEC units and traveling ionospheric perturbations (TIDs) moving away from the earthquake epicenter at an approximate speed of 277 *m*/*s*. To better study the localized variations of power in the TEC time series, a Paul wavelet analysis was performed [59, 60]. We find perturbation periods consistent with a tsunami typical deep ocean period. In particular, periods in the range of 10–30 min were obtained: these periods are similar to the ones of the tsunami ocean waves, which can range from 5 min up to an hour with the typical deep ocean period of only 10–30 wavelengths around 400 km and the velocity approximately 200 m/s.

**Figure 2** shows the sTEC time series wavelet analysis for the seven satellites in view at the station AHUP. The upper panels show the sTEC time series obtained with the VARION software in a real-time scenario. The bottom panels indicate the wavelet spectra. The colors represent the intensity of the power spectrum, and the black contour encloses regions of greater than 95% of confidence for a red noise process. We can identify five satellites (PRNs 4, 7, 8, 10, 20) with peaks consistent in time and period with the tsunami ocean waves. These results clearly show TIDs appearing after the tsunami reached the islands, with an increase of the power spectrum for periods between 10 and 30 min during the TIDs.

**Figure 3** shows time sTEC variations for 2 h (08:00–10:00 UT 28 October 2012) at the IPPs vs. distance from the Haida Gwaii earthquake epicenter, for the same seven satellites under consideration. The TIDs are clearly visible in the interval of significant sTEC variations (from positive to negative values and vice versa). The vertical and horizontal black lines represent the time (when the tsunami arrived at the Hawaii Islands) and the distance (between the epicenter and the Big Island), respectively. In this way, we identify the green rectangle as the alert area, and it is evident that satellite PRN 10, the closest to the earthquake epicenter, detected TIDs before the tsunami arrived at Hawaiian Islands (08:30:08 UT).

In the distance vs. time plots (also called hodochrons), the slope of the straight line, fitted considering corresponding sTEC minima for different satellites, represents the horizontal speed estimate of TIDs. This plot indicates that the linear least squares' estimated speed of the TIDs is about 316 m/s, and it is found to be in good

*the TIDs' mean propagation velocity. Figure adapted from Savastano et al. [5].*

*sTEC variations for 2 h (08:00–10:00 UT 28 October 2012) at the IPPs vs. distance from the Haida Gwaii earthquake epicenter, for the 7 satellites observed from the 56 Hawaii big islands GPS permanent stations. The TIDs are clearly visible in the interval of significant sTEC variations (from positive to negative values and vice versa). The vertical and horizontal black lines represent the time (when the tsunami arrived at the Hawaii Islands) and the distance (between the epicenter and the big island), respectively; it is evident that PRN 10 detected TIDs before the tsunami arrived at Hawaii Islands (08:30:08 UT). The slope of the straight line fitted, considering a linear least squares regression for corresponding sTEC minima for different satellites, represents*

*(a), (b), (e), (f) Four of 260 time series used for the wavelet analysis, station AHUP, satellite PRN 4, 7, 8, 10. (c), (d), (g), (h) the wavelet power spectrum used the Paul wavelet. The vertical axis displays the Fourier period (min), while the horizontal axis is time (s). The black vertical line represents the time when the tsunami reached the Hawaiian islands. The color panels represent the intensity of the power spectrum; the black contour encloses regions of greater than 95% confidence for a red noise process with a lag-1 coefficient of 0.72; the external black line indicates the cone of influence, the limit outside of which edge effects may become significant.*

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

**Figure 3.**

**49**

**Figure 2.**

*Figure adapted from Savastano et al. [5].*

#### *Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

#### **Figure 2.**

tsunami Method of Splitting Tsunami (MOST) model produced by the NOAA

*Map indicating the epicenter of the 10/27/2012 Canadian earthquake (left panel) and zoomed-in image of the Hawaii big island, where the 56 used GPS stations are located. Figure adapted from Savastano et al. [5].*

lengths around 400 km and the velocity approximately 200 m/s.

spectrum for periods between 10 and 30 min during the TIDs.

VARION processing outcame a TEC perturbation with amplitudes of up to 0.25 TEC units and traveling ionospheric perturbations (TIDs) moving away from the earthquake epicenter at an approximate speed of 277 *m*/*s*. To better study the localized variations of power in the TEC time series, a Paul wavelet analysis was performed [59, 60]. We find perturbation periods consistent with a tsunami typical deep ocean period. In particular, periods in the range of 10–30 min were obtained: these periods are similar to the ones of the tsunami ocean waves, which can range from 5 min up to an hour with the typical deep ocean period of only 10–30 wave-

**Figure 2** shows the sTEC time series wavelet analysis for the seven satellites in view at the station AHUP. The upper panels show the sTEC time series obtained with the VARION software in a real-time scenario. The bottom panels indicate the wavelet spectra. The colors represent the intensity of the power spectrum, and the black contour encloses regions of greater than 95% of confidence for a red noise process. We can identify five satellites (PRNs 4, 7, 8, 10, 20) with peaks consistent in time and period with the tsunami ocean waves. These results clearly show TIDs appearing after the tsunami reached the islands, with an increase of the power

**Figure 3** shows time sTEC variations for 2 h (08:00–10:00 UT 28 October 2012) at the IPPs vs. distance from the Haida Gwaii earthquake epicenter, for the same seven satellites under consideration. The TIDs are clearly visible in the interval of significant sTEC variations (from positive to negative values and vice versa). The vertical and horizontal black lines represent the time (when the tsunami arrived at the Hawaii Islands) and the distance (between the epicenter and the Big Island), respectively. In this way, we identify the green rectangle as the alert area, and it is evident that satellite PRN 10, the closest to the earthquake epicenter, detected TIDs before the tsunami arrived at Hawaiian Islands (08:30:08 UT).

Center for Tsunami Research [57, 58].

*Satellites Missions and Technologies for Geosciences*

*4.1.2 Results and discussion*

**Figure 1.**

**48**

*(a), (b), (e), (f) Four of 260 time series used for the wavelet analysis, station AHUP, satellite PRN 4, 7, 8, 10. (c), (d), (g), (h) the wavelet power spectrum used the Paul wavelet. The vertical axis displays the Fourier period (min), while the horizontal axis is time (s). The black vertical line represents the time when the tsunami reached the Hawaiian islands. The color panels represent the intensity of the power spectrum; the black contour encloses regions of greater than 95% confidence for a red noise process with a lag-1 coefficient of 0.72; the external black line indicates the cone of influence, the limit outside of which edge effects may become significant. Figure adapted from Savastano et al. [5].*

#### **Figure 3.**

*sTEC variations for 2 h (08:00–10:00 UT 28 October 2012) at the IPPs vs. distance from the Haida Gwaii earthquake epicenter, for the 7 satellites observed from the 56 Hawaii big islands GPS permanent stations. The TIDs are clearly visible in the interval of significant sTEC variations (from positive to negative values and vice versa). The vertical and horizontal black lines represent the time (when the tsunami arrived at the Hawaii Islands) and the distance (between the epicenter and the big island), respectively; it is evident that PRN 10 detected TIDs before the tsunami arrived at Hawaii Islands (08:30:08 UT). The slope of the straight line fitted, considering a linear least squares regression for corresponding sTEC minima for different satellites, represents the TIDs' mean propagation velocity. Figure adapted from Savastano et al. [5].*

In the distance vs. time plots (also called hodochrons), the slope of the straight line, fitted considering corresponding sTEC minima for different satellites, represents the horizontal speed estimate of TIDs. This plot indicates that the linear least squares' estimated speed of the TIDs is about 316 m/s, and it is found to be in good

two GEO satellites are shown. The raw GEO observations are available in RINEX

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

The ionosonde observations from site PA836 (located less than 5 kilometers from the Vandenberg Air force Base) are used here for an independent comparison with the VARION-GEO solutions. The electron density profiles derived from the sweeping ionosonde observations extend from the lower E region to the F region

**Figure 6(a)** shows the closest VARION-GEO Δ*sTEC* time series to the ionosonde site and (b) shows the ionosonde peak electron density (NmF2)

*Map showing the IPP location for satellites S35 (blue dots) and S38 (yellow dots) seen from the 62 GNSS stations. The IPPs for GEO satellites can be considered to be fixed over time. The red dot represents the location of the ionosonde site PA836. On the right, we display two maps representing the earth as seen from WAAS-GEO*

*(a) Shows the VARION-GEO* Δ*sTEC solutions obtained from station p215, satellite S38. (b) Shows the NmF2 time variability obtained from ionosonde PA836. (c) Shows the down-sampled and normalized* Δ*sTEC solutions (red curve) and the normalized NmF2 time series (blue curve) plotted using a common scale [0, 1]. This figure shows a high correlation between the VARION-GEO* Δ*sTEC solutions and ionosonde data. The correlation coefficient between the two curves is 0.97. Figure adapted from Savastano et al. [53].*

*satellites S35 and S38. Figure adapted from Savastano et al. [53].*

format with a sampling rate of 15 seconds.

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

peak with 15 min of cadence.

*4.2.2 Results and discussion*

**Figure 5.**

**Figure 6.**

**51**

#### **Figure 4.**

*Space–time sTEC variations at six epochs within the 2-h interval (08:00–10:00 UT 28 October 2012) at the SIPs for the five satellites showing TIDs over-plotted the tsunami MOST model. TIDs are consistent in time and space with the tsunami waves. Figure adapted from Savastano et al. [5].*

agreement with a typical speed of the tsunami gravity waves estimated with ground-based GNSS receivers.

**Figure 4** displays a sequence of maps of the region around the Hawaiian Islands showing the variations in sTEC (determinable in real time) at IPP/SPI locations on top of the MOST model sea-surface heights. Note that, just as the MOST model wavefronts are moving past the IPPs, the sTEC variations in the region become pronounced, correlated with the passage of the ocean tsunami itself. In particular, at 08:22:00 GPS time (08:21:44 UT), we are able to see sTEC perturbations from 56 stations looking at satellite PRN 10. The propagation of the MOST modeled tsunami passes the ionospheric pierce points located NW of the Big Island and offers insight with regard to the ionospheric response to the tsunami-driven atmospheric gravity wave. These perturbations are detected before the tsunami reached the islands as seen from the locations of the SIP points. The following frames indicate the tsunami-driven TIDs detected from the other four satellites (PRNs 4, 7, 8, 20) tracking the propagating tsunami (see supplementary video SV1 online).

#### **4.2 Falcon 9 rocket-induced ionospheric plasma depletion**

#### *4.2.1 Dataset*

To estimate the slant TEC variations associated with the rocket launch, we applied the VARION algorithm to the WAAS-GEO observations collected at 62 Plate Boundary Observatory (PBO) sites located in California (https://www.unavc o.org/instrumentation/networks/status/pbohttps://www.unavco.org/instrumenta tion/networks/status/pbo). In this study, we used satellite S35 (PRN 135) located at 133 degree West and satellite S38 (PRN 138) located at 107.3 degree West. **Figure 5** (left panel) shows the IPP location for satellites S35 (blue dots) and S38 (yellow dots) and the location of the ionosonde site PA836 (red dot). We use the standard single-shell ionospheric layer approximation at the height of 300 km to calculate the IPP locations [61]. On the right, two maps representing the Earth as seen from these

#### *Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

two GEO satellites are shown. The raw GEO observations are available in RINEX format with a sampling rate of 15 seconds.

The ionosonde observations from site PA836 (located less than 5 kilometers from the Vandenberg Air force Base) are used here for an independent comparison with the VARION-GEO solutions. The electron density profiles derived from the sweeping ionosonde observations extend from the lower E region to the F region peak with 15 min of cadence.

#### *4.2.2 Results and discussion*

**Figure 6(a)** shows the closest VARION-GEO Δ*sTEC* time series to the ionosonde site and (b) shows the ionosonde peak electron density (NmF2)

#### **Figure 5.**

agreement with a typical speed of the tsunami gravity waves estimated with

*space with the tsunami waves. Figure adapted from Savastano et al. [5].*

*Satellites Missions and Technologies for Geosciences*

**4.2 Falcon 9 rocket-induced ionospheric plasma depletion**

To estimate the slant TEC variations associated with the rocket launch, we applied the VARION algorithm to the WAAS-GEO observations collected at 62 Plate Boundary Observatory (PBO) sites located in California (https://www.unavc o.org/instrumentation/networks/status/pbohttps://www.unavco.org/instrumenta tion/networks/status/pbo). In this study, we used satellite S35 (PRN 135) located at 133 degree West and satellite S38 (PRN 138) located at 107.3 degree West. **Figure 5** (left panel) shows the IPP location for satellites S35 (blue dots) and S38 (yellow dots) and the location of the ionosonde site PA836 (red dot). We use the standard single-shell ionospheric layer approximation at the height of 300 km to calculate the IPP locations [61]. On the right, two maps representing the Earth as seen from these

**Figure 4** displays a sequence of maps of the region around the Hawaiian Islands showing the variations in sTEC (determinable in real time) at IPP/SPI locations on top of the MOST model sea-surface heights. Note that, just as the MOST model wavefronts are moving past the IPPs, the sTEC variations in the region become pronounced, correlated with the passage of the ocean tsunami itself. In particular, at 08:22:00 GPS time (08:21:44 UT), we are able to see sTEC perturbations from 56 stations looking at satellite PRN 10. The propagation of the MOST modeled tsunami passes the ionospheric pierce points located NW of the Big Island and offers insight with regard to the ionospheric response to the tsunami-driven atmospheric gravity wave. These perturbations are detected before the tsunami reached the islands as seen from the locations of the SIP points. The following frames indicate the tsunami-driven TIDs detected from the other four satellites (PRNs 4, 7, 8, 20) tracking the propagating tsunami (see supplementary video SV1 online).

*Space–time sTEC variations at six epochs within the 2-h interval (08:00–10:00 UT 28 October 2012) at the SIPs for the five satellites showing TIDs over-plotted the tsunami MOST model. TIDs are consistent in time and*

ground-based GNSS receivers.

**Figure 4.**

*4.2.1 Dataset*

**50**

*Map showing the IPP location for satellites S35 (blue dots) and S38 (yellow dots) seen from the 62 GNSS stations. The IPPs for GEO satellites can be considered to be fixed over time. The red dot represents the location of the ionosonde site PA836. On the right, we display two maps representing the earth as seen from WAAS-GEO satellites S35 and S38. Figure adapted from Savastano et al. [53].*

#### **Figure 6.**

*(a) Shows the VARION-GEO* Δ*sTEC solutions obtained from station p215, satellite S38. (b) Shows the NmF2 time variability obtained from ionosonde PA836. (c) Shows the down-sampled and normalized* Δ*sTEC solutions (red curve) and the normalized NmF2 time series (blue curve) plotted using a common scale [0, 1]. This figure shows a high correlation between the VARION-GEO* Δ*sTEC solutions and ionosonde data. The correlation coefficient between the two curves is 0.97. Figure adapted from Savastano et al. [53].*

represents an important outreach. This chapter finds its reasons in this background, but in the meantime it extends its fields of application to natural hazard early warning systems. It represents an overview about the possible real-time VARION applications for the monitoring of ionospheric irregularities and TEC perturbations. The VARION is based on single time difference of geometry-free combination of

carrier-phase observations that makes it suitable for real-time application. The VARION algorithm was applied both to standard GNSS MEO satellites and to GNSS GEO satellites. It is important to underline that these analyses were carried out in

valid contribution for the enhancement of tsunami early warning system.

In [53], it was demonstrated that the extension of the VARION algorithm to GEO satellites enabled a better description of the ionospheric plasma depletion induced by a Falcon 9 rocket. These results are relevant for different GNSS applications, since an ionospheric plasma depletion can potentially lead to a range error of several meters. Lastly, the VARION was implemented in the JPL's Global Differential GPS System (GDGPS) real-time interface that may be accessed at (https:// iono2la.gdgps.net/), allowing real-time monitoring of the status of the ionosphere. Therefore, the VARION extreme versatility makes it suitable for real-time iono-

The authors thank Prof. Mattia Crespi for his great support throughout of the

VARION Variometric Approach for Real-Time Ionosphere Observation

In detail, the 2012 Haida Gwaii tsunami event represents a fundamental study case as it showed for the first time that real-time detection of tsunami-induced TEC perturbations is possible and that these TIDs become clear before the tsunami waves hit the Hawaii Big Island [5]. This paper demonstrated that real-time GNSS tracking of TEC perturbations can provide information on tsunami propagation that is consistent with that generated by NOAA's current real-time forecast system [62]. The ability of VARION to detect the TIDs before the tsunami arrival represents a

real-time scenario: only data available in real time were used.

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

spheric monitoring and anomaly detection applications.

The authors declare no conflict of interest.

TIDs traveling ionospheric disturbances CIDs coseismic ionospheric disturbances

PBO Plate Boundary Observatory network WAAS Wide Area Augmentation System MOST Method of Splitting Tsunami

IPP ionospheric pierce point SIP sub-ionospheric pierce point

MEO medium Earth orbit GEO geostationary orbit

AGWs atmospheric gravity waves SAWs shock acoustic waves TEC total electron content

**Acknowledgements**

**Conflict of interest**

**Abbreviations**

**53**

drawing up of this chapter.

#### **Figure 7.**

*Space–time* Δ*sTEC variations for 30 min after the launch (one frame every 5 min) at the SIPs (same positions of the corresponding IPPs on the map) for the 2 GEO satellites (square symbols) and 6 GPS satellites (denoted by circles) seen from the 62 GNSS permanent stations. The ionospheric hole is detected from both GEO satellites 5 min after the rocket launch. The coordinates are expressed in geodetic latitude (in degrees north) and longitude (in degrees west). Figure adapted from Savastano et al. [53].*

extracted for each electron density profile measured by the ionosonde and plotted as a function of time. An electron density depletion in the F2 layer is clearly visible from both data sets. In order to quantify the agreement between the two curves, we applied a min-max normalization to the two curves to bring all values into the range [0, 1]. This procedure allows us to study the correlation between the two curves: **Figure 6(c)** displays the normalized Δ*sTEC* (red) and NmF2 (blue) curves. We then down-sampled the normalized VARION-GEO solutions in order to have the same sampling rate as the ionosonde data (15 min). Finally, we computed the correlation coefficient between the two curves, and we found a value of 0.97. Despite the fact the ionosonde electron density profiles extend up to the F2 peak, and that two measurements are not exactly co-located, the agreement between the two datasets is very good.

**Figure 7** displays a sequence of six maps (every 5 min) in the region around Vandenberg Air Force Base in California. These maps show the VARION-GEO Δ*sTEC* solutions at GEO-IPP locations for satellites S35 and S38 (squared markers) and the VARION-GPS solutions for satellites G02, G05, G06, G12, G25, and G29 (circle markers). The colors represent variations in the Δ*sTEC*. The ionospheric hole (blue color) is clearly detected from both GEO satellites 5 min after the rocket launch. The GPS satellites start detecting the ionospheric hole as they are moving inside the depleted ionospheric region. This figure well illustrates the difference between GEO and GPS solutions. VARION-GEO solutions provide a direct estimation of the time evolution of the ionosphere over a fixed location, while VARION-GPS solutions are also affected by the ionospheric spatial gradients as they move along the IPP trajectory (Section 3.2). The figure shows the potential benefits of GEO satellites as a complementary technique for well-established GPS satellites.

#### **5. Conclusions**

It is widely known that ionospheric anomalies can be a threat to GNSS and satellite telecommunications; therefore real-time monitoring of the ionosphere

#### *Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

represents an important outreach. This chapter finds its reasons in this background, but in the meantime it extends its fields of application to natural hazard early warning systems. It represents an overview about the possible real-time VARION applications for the monitoring of ionospheric irregularities and TEC perturbations.

The VARION is based on single time difference of geometry-free combination of carrier-phase observations that makes it suitable for real-time application. The VARION algorithm was applied both to standard GNSS MEO satellites and to GNSS GEO satellites. It is important to underline that these analyses were carried out in real-time scenario: only data available in real time were used.

In detail, the 2012 Haida Gwaii tsunami event represents a fundamental study case as it showed for the first time that real-time detection of tsunami-induced TEC perturbations is possible and that these TIDs become clear before the tsunami waves hit the Hawaii Big Island [5]. This paper demonstrated that real-time GNSS tracking of TEC perturbations can provide information on tsunami propagation that is consistent with that generated by NOAA's current real-time forecast system [62]. The ability of VARION to detect the TIDs before the tsunami arrival represents a valid contribution for the enhancement of tsunami early warning system.

In [53], it was demonstrated that the extension of the VARION algorithm to GEO satellites enabled a better description of the ionospheric plasma depletion induced by a Falcon 9 rocket. These results are relevant for different GNSS applications, since an ionospheric plasma depletion can potentially lead to a range error of several meters. Lastly, the VARION was implemented in the JPL's Global Differential GPS System (GDGPS) real-time interface that may be accessed at (https:// iono2la.gdgps.net/), allowing real-time monitoring of the status of the ionosphere.

Therefore, the VARION extreme versatility makes it suitable for real-time ionospheric monitoring and anomaly detection applications.

#### **Acknowledgements**

extracted for each electron density profile measured by the ionosonde and plotted as a function of time. An electron density depletion in the F2 layer is clearly visible from both data sets. In order to quantify the agreement between the two curves, we applied a min-max normalization to the two curves to bring all values into the range [0, 1]. This procedure allows us to study the correlation between the two curves: **Figure 6(c)** displays the normalized Δ*sTEC* (red) and NmF2 (blue) curves. We then down-sampled the normalized VARION-GEO solutions in order to have the same sampling rate as the ionosonde data (15 min). Finally, we computed the correlation coefficient between the two curves, and we found a value of 0.97. Despite the fact the ionosonde electron density profiles extend up to the F2 peak, and that two measurements are not exactly co-located, the agreement between the

*longitude (in degrees west). Figure adapted from Savastano et al. [53].*

*Satellites Missions and Technologies for Geosciences*

*Space–time* Δ*sTEC variations for 30 min after the launch (one frame every 5 min) at the SIPs (same positions of the corresponding IPPs on the map) for the 2 GEO satellites (square symbols) and 6 GPS satellites (denoted by circles) seen from the 62 GNSS permanent stations. The ionospheric hole is detected from both GEO satellites 5 min after the rocket launch. The coordinates are expressed in geodetic latitude (in degrees north) and*

**Figure 7** displays a sequence of six maps (every 5 min) in the region around Vandenberg Air Force Base in California. These maps show the VARION-GEO Δ*sTEC* solutions at GEO-IPP locations for satellites S35 and S38 (squared markers) and the VARION-GPS solutions for satellites G02, G05, G06, G12, G25, and G29 (circle markers). The colors represent variations in the Δ*sTEC*. The ionospheric hole (blue color) is clearly detected from both GEO satellites 5 min after the rocket launch. The GPS satellites start detecting the ionospheric hole as they are moving inside the depleted ionospheric region. This figure well illustrates the difference between GEO and GPS solutions. VARION-GEO solutions provide a direct estimation of the time evolution of the ionosphere over a fixed location, while VARION-GPS solutions are also affected by the ionospheric spatial gradients as they move along the IPP trajectory (Section 3.2). The figure shows the potential benefits of GEO satellites as a complementary technique for well-established GPS satellites.

It is widely known that ionospheric anomalies can be a threat to GNSS and satellite telecommunications; therefore real-time monitoring of the ionosphere

two datasets is very good.

**Figure 7.**

**5. Conclusions**

**52**

The authors thank Prof. Mattia Crespi for his great support throughout of the drawing up of this chapter.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Abbreviations**


*Satellites Missions and Technologies for Geosciences*

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Gavrilyuk NS, Ishin AB, et al. A review of GPS/GLONASS studies of the ionospheric response to natural and anthropogenic processes and

*DOI: http://dx.doi.org/10.5772/intechopen.90036*

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations*

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#### **Author details**

Giorgio Savastano<sup>1</sup> \*† and Michela Ravanelli<sup>2</sup> \*†

1 Spire Global, Inc., Luxembourg

2 Geodesy and Geomatics Division – DICEA, Sapienza University of Rome, Rome, Italy

\*Address all correspondence to: giorgio.savastano@spire.com and michela.ravanelli@uniroma1.it

† These authors contributed equally.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Real-Time Monitoring of Ionospheric Irregularities and TEC Perturbations DOI: http://dx.doi.org/10.5772/intechopen.90036*

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**Author details**

Giorgio Savastano<sup>1</sup>

Italy

**54**

1 Spire Global, Inc., Luxembourg

*Satellites Missions and Technologies for Geosciences*

and michela.ravanelli@uniroma1.it

† These authors contributed equally.

provided the original work is properly cited.

\*† and Michela Ravanelli<sup>2</sup>

\*Address all correspondence to: giorgio.savastano@spire.com

\*†

2 Geodesy and Geomatics Division – DICEA, Sapienza University of Rome, Rome,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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[54] Bishop G, Walsh D, Daly P, Mazzella A, Holland E. Analysis of the temporal stability of GPS and GLONASS group delay correction terms seen in various sets of ionospheric delay data. In: Proceedings of the 7th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1994). 1994. pp. 1653-1661

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Section 2

Impacts on GNSS: Modeling

and Mitigation Techniques

**59**

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### Section 2

## Impacts on GNSS: Modeling and Mitigation Techniques

Research: Space Physics. 2010;**115**(A9).

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[60] Misiti M, Misiti Y, Oppenheim G, Poggi J-M. Wavelet Toolbox. Vol. 15. Natick, MA: The MathWorks Inc.;

[61] Klobuchar JA. Ionospheric timedelay algorithm for single-frequency GPS users. IEEE Transactions on

[62] Murray JR, Bartlow N, Bock Y, Brooks BA, Foster J, Freymueller J, et al. Regional global navigation satellite system networks for crustal

deformation monitoring. Seismological

Aerospace and Electronic Systems. 1987;

[58] Wei Y, Chamberlin C, Titov V, Tang L, Bernard EN. Modeling of the 2011 Japan tsunami - lessons for nearfield forecast. Pure and Applied Geophysics. 2013;**170**(6–8):1309-1331

L04609

**79**:6178

1996. p. 21

**AES-23**(3):325-331

Research Letters. 2019

[50] Komjathy A, Sparks L, Wilson BD,

processing of more than 1000 groundbased GPS receivers for studying intense ionospheric storms. Radio

Mannucci AJ. Automated daily

[51] Sardon E, Rius A, Zarraoa N. Estimation of the transmitter and receiver differential biases and the ionospheric total electron content from global positioning system observations.

Radio Science. 1994;**29**:577-586

Vergados P, Ravanelli M,

[54] Bishop G, Walsh D, Daly P, Mazzella A, Holland E. Analysis of the temporal stability of GPS and GLONASS group delay correction terms seen in various sets of ionospheric delay data. In: Proceedings of the 7th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 1994). 1994. pp. 1653-1661

[55] Demyanov VV, Yasyukevich YV, Jin S, et al. The second-order derivative of gps carrier phase as a promising means for ionospheric scintillation research. Pure and Applied Geophysics.

[56] Savastano G. New applications and

challenges of GNSS variometric approach [Ph.D. dissertation]. Rome, Italy: Dept. DICEA, Univ. La Sapienza;

2019;**176**(10):4555-4573

2018

**58**

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[53] Savastano G, Komjathy A, Shume E,

Verkhoglyadova O, et al. Advantages of geostationary satellites for ionospheric anomaly studies: Ionospheric plasma depletion following a rocket launch. Remote Sensing. 2019;**11**(14):1734

Article number: A09314

Science. 2005;**40**:RS6006

**Chapter 4**

**Abstract**

are difficult to measure.

**1. Introduction**

**61**

range, multipath error, ionospheric delay

GPS Signal Multipath Error

*Mogadala Vinod Kumar and Gottapu Sasibhushana Rao*

The performance of GPS receiver depends on the accuracy of the range measurements. The predominant errors in range measurements are due to propagation path delays, making the measured range longer than it would be, if the signal has not reflected or refracted while propagating. In this chapter, an algorithm is proposed to mitigate the multipath error on the pseudorange measured from L1 carrier frequency. The error is estimated considering the linear combination of the GPS measurements and carrier frequencies of L band, viz. L1 and L2. This algorithm exploits the random nature of the multipath error and it avoids complex calculations involving sensitive parameter like reflection coefficient of the nearby reflectors. The multipath error is mitigated for standalone GPS receiver located in Indian subcontinent. Implementation of the algorithm shows pseudorange error due to multipath varied from 7 to 52 m, where the signals of low elevation satellites are most affected. GPS receiver position is calculated by considering multipath error corrected pseudoranges of all the visible satellites. This resulted in maximum error reduction of 30 m in receiver position estimates. This mitigation technique will be useful in selecting the site for GPS receiving antenna, where reflection coefficients

**Keywords:** GPS, L1 and L2 frequencies, elevation angle, pseudorange, carrier phase

GPS finds its applications in most of the day to day activities of human life, viz. precise farming, surveying, missile guidance, military and civil aviation [1]. However, the accuracy, availability, reliability, and integrity of GPS navigation solution are impaired by various errors which are originating at the satellites, like orbital errors, satellite clock errors, etc. [2]; whereas, the receiver clock errors, multipath errors, receiver noise, and antenna phase center variations are the errors originating at the receiver [3]. Also, the propagation medium contributes to the delays in the GPS signal, as it passes through the ionosphere and troposphere [4]. In addition to these errors, the accuracy of the navigation solution is also affected by GPS satellites location as viewed by the receiver. Hence, error estimation and

correction is a primary concern in precise navigation applications. In this chapter,

Mitigation Technique

*Bharati Bidikar, Babji Prasad Chapa,*

#### **Chapter 4**

## GPS Signal Multipath Error Mitigation Technique

*Bharati Bidikar, Babji Prasad Chapa, Mogadala Vinod Kumar and Gottapu Sasibhushana Rao*

#### **Abstract**

The performance of GPS receiver depends on the accuracy of the range measurements. The predominant errors in range measurements are due to propagation path delays, making the measured range longer than it would be, if the signal has not reflected or refracted while propagating. In this chapter, an algorithm is proposed to mitigate the multipath error on the pseudorange measured from L1 carrier frequency. The error is estimated considering the linear combination of the GPS measurements and carrier frequencies of L band, viz. L1 and L2. This algorithm exploits the random nature of the multipath error and it avoids complex calculations involving sensitive parameter like reflection coefficient of the nearby reflectors. The multipath error is mitigated for standalone GPS receiver located in Indian subcontinent. Implementation of the algorithm shows pseudorange error due to multipath varied from 7 to 52 m, where the signals of low elevation satellites are most affected. GPS receiver position is calculated by considering multipath error corrected pseudoranges of all the visible satellites. This resulted in maximum error reduction of 30 m in receiver position estimates. This mitigation technique will be useful in selecting the site for GPS receiving antenna, where reflection coefficients are difficult to measure.

**Keywords:** GPS, L1 and L2 frequencies, elevation angle, pseudorange, carrier phase range, multipath error, ionospheric delay

#### **1. Introduction**

GPS finds its applications in most of the day to day activities of human life, viz. precise farming, surveying, missile guidance, military and civil aviation [1]. However, the accuracy, availability, reliability, and integrity of GPS navigation solution are impaired by various errors which are originating at the satellites, like orbital errors, satellite clock errors, etc. [2]; whereas, the receiver clock errors, multipath errors, receiver noise, and antenna phase center variations are the errors originating at the receiver [3]. Also, the propagation medium contributes to the delays in the GPS signal, as it passes through the ionosphere and troposphere [4]. In addition to these errors, the accuracy of the navigation solution is also affected by GPS satellites location as viewed by the receiver. Hence, error estimation and correction is a primary concern in precise navigation applications. In this chapter,

the error originating at the receiver which is due to GPS signal multiple paths is addressed.

• Errors originating in the propagation medium like ionospheric delay and

• Errors originating at the receiver like multipath error, receiver clock error, and

The major error sources their impact on PGS range measurements are given in

A GPS signal may take several paths to a receiver's antenna and the signal can be reflected from buildings or ground and interfere with the direct signal creating a range error of several meters or more. The error impact on pseudorange measurement is much larger than that on the carrier phase. The inaccuracy in pseudorange directly affects the receiver position estimation. But for carrier phase measurements, the inaccuracy due to multipath will lead to a wide ambiguity search space and hence takes a longer time to resolve the ambiguity. This will result in incorrect determination of initial ambiguity which further leads to positioning errors. The signal delay due to multipath is very sensitive to the reflection coefficients of the nearby reflectors. These parameters limit the efficiency of the multipath modeling techniques. The impact of the multipath error on GPS satellite signal is given in the

GPS receiver determines the pseudorange by code tracking and carrier tracking method. Code tracking method estimates the propagation time and carrier phase tracking method estimates phase delay between the received carrier and the locally generated signal. To measure the propagation time, i.e., the code range measurement, the locally generated code is shifted in time and correlated with the received signal. The correlation parameter is used by a discriminator to adjust the locally generated code with the received code and obtain the time delay. This time delay when scaled by the speed of light gives the range between the satellite and the receiver. A common code discriminator function used in GNSS receivers is the early correlator (E) minus Late correlator (L) values (E-L). The early correlator value is

Orbit 1–5 Error in broadcast ephemeris due to residual errors in curve fitting

Code multipath 15–150 Maximum 150 m using one chip correlator spacing and 15 m using

Code noise 0.1–3 For C/A code. Depends upon receiver technology and dynamic

Carrier multipath 0.001–0.03 Maximum 4.75 cm for L1 carrier and 6.11 cm for L2 carrier Carrier noise 0.0002–0.002 For L1 carrier. Depends upon receiver technology and dynamic

Ionosphere 2–50 Depends upon satellite elevation angle and solar activity Troposphere 2–30 Depends upon the water vapor content in the lower part of

Clock 3–5 Due to satellite clock drift

**Remarks**

atmosphere

0.1 chip correlator spacing

stress

stress

neutral atmospheric delay

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

instrument biases.

**Table 1** [26].

following sub section.

**Error source Nominal**

**Table 1.**

**63**

*GPS error sources for SPS receivers.*

**values (m)**

**2.1 Multipath effect on GPS measurements**

The signal transmitted by the satellite, taking multiple paths, affects both the pseudorange and carrier phase measurements [5]. But the effect of multipath on pseudorange is much higher than the carrier phase [6, 7]. The pseudorange multipath error in an urban environment is characterized by considering signal to noise ratio and elevation angle for DGPS [8]. The multipath effect on carrier phase measurements was also detected [9]. The GPS receiver cannot distinguish the direct signal from the several multipath signals. This problem in tracking loop was addressed by several authors [10, 11]. For the same GPS receiver, multipath error differs depending on the reflecting surfaces, viz. multipath effect due to large water bodies like sea surface. Multipath due to water bodies and its impact on the precision of GNSS positioning in marine application was also studied [12] and similar studied were done for static and kinematic receivers [13]. There are several studies mitigating the multipath error by antenna based mitigation methods. A choke ring antenna with ground plane to absorb multipath signals was proved to mitigate the error to large extent [14, 15]. Mitigation of the error and the performance of the receivers are analyzed for dual frequency receiver as well [16]. Some techniques also rely on the analysis of the signal-to-noise ratio values of GPS signals [17]. Apart from these methods the filter-based techniques are also implemented to extract or eliminate multipath effects, such as wavelet filters [18–20], Vondrak filter [21] and adaptive filter [22]. In precise positioning applications, multipath is a major error source and impact needs to be calculated especially in urban canyon while setting up GPS receiver antenna [23]. The multipath error originating at the receiver is very sensitive to geometry (like size and surface texture) and the reflection coefficients of the nearby reflectors [24]. These parameters limit the efficiency of the conventional multipath modeling methods. But in this chapter, an algorithm is proposed to calculate the multipath error affecting GPS L1 pseudorange range measurement. The algorithm utilizes the relationship between the code range measurements, carrier phase measurements and carrier frequencies (L1 = 1575.42 MHz and L2 = 1227.60 MHz) [25]. In this chapter, the multipath error affecting the pseudorange measurements of Satellite Vehicle Pseudorandom Noise (SVPRN) 07, 23, 28 and 31 are estimated using the proposed algorithm. The ephemerides data of these satellites for the entire day was collected on March 11, 2019, from a standalone GPS receiver, located in the Indian subcontinent. The proposed algorithm and the impact analysis done in this chapter will also be a valuable aid for setting up the GPS receiver antenna for air traffic control and navigation. Section 2 briefly explains the error budget and explains multipath error in detail. It also gives brief overview of the existing multipath error mitigation techniques. Section 3 gives the proposed multipath error estimation algorithm. Finally, the results and conclusions are given in Sections 4 and 5 respectively.

#### **2. Multipath error in GPS**

GPS measurements are biased by many errors. These errors are specific to each satellite signal and translate into a receiver position error (the receiver position being calculated from the estimated travel time of the signal from each satellite to the receiver). The errors are divided into three major groups as,

• Errors originating at the satellite like satellite clock error, ephemeris error, and error due to orbital eccentricity

the error originating at the receiver which is due to GPS signal multiple paths is

signal from the several multipath signals. This problem in tracking loop was addressed by several authors [10, 11]. For the same GPS receiver, multipath error differs depending on the reflecting surfaces, viz. multipath effect due to large water bodies like sea surface. Multipath due to water bodies and its impact on the precision of GNSS positioning in marine application was also studied [12] and similar studied were done for static and kinematic receivers [13]. There are several studies mitigating the multipath error by antenna based mitigation methods. A choke ring antenna with ground plane to absorb multipath signals was proved to mitigate the error to large extent [14, 15]. Mitigation of the error and the performance of the receivers are analyzed for dual frequency receiver as well [16]. Some techniques also rely on the analysis of the signal-to-noise ratio values of GPS signals [17]. Apart from these methods the filter-based techniques are also implemented to extract or eliminate multipath effects, such as wavelet filters [18–20], Vondrak filter [21] and adaptive filter [22]. In precise positioning applications, multipath is a major error source and impact needs to be calculated especially in urban canyon while setting up GPS receiver antenna [23]. The multipath error originating at the receiver is very sensitive to geometry (like size and surface texture) and the reflection coefficients of the nearby reflectors [24]. These parameters limit the efficiency of the conventional multipath modeling methods. But in this chapter, an algorithm is proposed to calculate the multipath error affecting GPS L1 pseudorange range measurement. The algorithm utilizes the relationship between the code range measurements, carrier phase measurements and carrier frequencies (L1 = 1575.42 MHz and L2 = 1227.60 MHz) [25]. In this chapter, the multipath error affecting the

pseudorange measurements of Satellite Vehicle Pseudorandom Noise (SVPRN) 07, 23, 28 and 31 are estimated using the proposed algorithm. The ephemerides data of these satellites for the entire day was collected on March 11, 2019, from a standalone GPS receiver, located in the Indian subcontinent. The proposed algorithm and the impact analysis done in this chapter will also be a valuable aid for setting up the GPS receiver antenna for air traffic control and navigation. Section 2 briefly explains the error budget and explains multipath error in detail. It also gives brief overview of the existing multipath error mitigation techniques. Section 3 gives the proposed multipath error estimation algorithm. Finally, the results and conclusions are given

GPS measurements are biased by many errors. These errors are specific to each satellite signal and translate into a receiver position error (the receiver position being calculated from the estimated travel time of the signal from each satellite to

• Errors originating at the satellite like satellite clock error, ephemeris error, and

the receiver). The errors are divided into three major groups as,

in Sections 4 and 5 respectively.

**2. Multipath error in GPS**

**62**

error due to orbital eccentricity

The signal transmitted by the satellite, taking multiple paths, affects both the pseudorange and carrier phase measurements [5]. But the effect of multipath on pseudorange is much higher than the carrier phase [6, 7]. The pseudorange multipath error in an urban environment is characterized by considering signal to noise ratio and elevation angle for DGPS [8]. The multipath effect on carrier phase measurements was also detected [9]. The GPS receiver cannot distinguish the direct

addressed.

*Satellites Missions and Technologies for Geosciences*


The major error sources their impact on PGS range measurements are given in **Table 1** [26].

A GPS signal may take several paths to a receiver's antenna and the signal can be reflected from buildings or ground and interfere with the direct signal creating a range error of several meters or more. The error impact on pseudorange measurement is much larger than that on the carrier phase. The inaccuracy in pseudorange directly affects the receiver position estimation. But for carrier phase measurements, the inaccuracy due to multipath will lead to a wide ambiguity search space and hence takes a longer time to resolve the ambiguity. This will result in incorrect determination of initial ambiguity which further leads to positioning errors. The signal delay due to multipath is very sensitive to the reflection coefficients of the nearby reflectors. These parameters limit the efficiency of the multipath modeling techniques. The impact of the multipath error on GPS satellite signal is given in the following sub section.

#### **2.1 Multipath effect on GPS measurements**

GPS receiver determines the pseudorange by code tracking and carrier tracking method. Code tracking method estimates the propagation time and carrier phase tracking method estimates phase delay between the received carrier and the locally generated signal. To measure the propagation time, i.e., the code range measurement, the locally generated code is shifted in time and correlated with the received signal. The correlation parameter is used by a discriminator to adjust the locally generated code with the received code and obtain the time delay. This time delay when scaled by the speed of light gives the range between the satellite and the receiver. A common code discriminator function used in GNSS receivers is the early correlator (E) minus Late correlator (L) values (E-L). The early correlator value is


**Table 1.** *GPS error sources for SPS receivers.* the correlation between the incoming code and an early version of the locally generated code. The Late correlator value is the correlation value between the incoming code and a late version of the locally generated code. In the presence of multipath, the time delay is estimated by correlating the composite signal with locally generate code(s), which result in code measurement errors. The impact of multipath on code phase measurements can be up to half a chip equivalent to a range error of about 150 m for the GPS C/A code.

In a GPS receiver, the carrier phase is measured by accumulating the phase of the numerically controlled oscillator (NCO) output. In an environment, where there are no reflected signals, the incoming signal carrier is the same as the direct signal carrier. The NCO generated local carrier locks onto the direct carrier very accurately, and, as a result, the true phase difference between the incoming signal carrier and the locally generated carrier is nearly zero, (actually zero mean), at steady state. The resulting phase measurements are very accurate. In the presence of multipath, however, the composite signal phase shifts from the direct signal phase, and the NCO-generated local carrier locks onto the composite carrier phase, resulting in an error in the phase measurement. The processing of the received signal in GPS receiver is shown below.

The change in carrier phase due to multipath effect can be determined in the PLL section of the GPS receiver as shown in **Figure 1**. Following steps are carried out to extract the error in phase due to multipath,

Step 1: The direct signal received at the receiver is *Acosφ*. Here the "*A"* is amplitude and "*φ*" is phase angle.

Step 2: The reflected signal is modeled as *α*ð Þ *Acosφ* þ Δ*φ* , where "*α*" is attenuation constant and "Δ*φ*" is change in carrier phase of the maximum reflected signal.

Step 3: The composite signal received as the GPS receiver is

$$
\begin{array}{|c|c|c|c|}
\hline
\text{ } & \text{Antenna} & & \\
\hline
 & \text{RF front} & \text{Down} & \\
\hline
 & \text{end} & \text{convolution} & \begin{array}{|c|c|}
\hline
\text{Down} & & \\
\hline
\text{cororder} & & \\
\hline
\text{cororder} & & \\
\hline
 & \text{reroval} & \\
\hline
 & & \\
\hline
\text{scollator} & & \\
\hline
\text{scollator} & & \\
\hline
\text{scollator} & & \\
\hline
\text{sinclator} & & \\
\hline
\text{sinclator} & & \\
\hline
\text{sinclator} & & \\
\hline
\text{sinclator} & & \\
\hline
\text{sinclator} & & \\
\hline
\end{array}
\qquad
\begin{array}{|c|c|}
\hline
\text{DLL} & & \\
\hline
 & \text{Correlator} & \\
\hline
 & \text{Coble \& carrier} & \\
\hline
 & \text{passure} & \\
\hline
 & \text{NCO} & \\
\hline
 & \text{NCO} & \\
\hline
 & \text{NCO} & \\
\hline
 & \text{NCO} & \\
\hline
 & \text{NCO} & \\
\hline
 & \text{sinclator} & \\
\hline
\end{array}
$$

$$A\cos\varphi + a(A\cos\varphi + \Delta\varphi) \tag{1}$$

Step 4: The error in phase measurement due to multipath is modeled as,

*δφ* <sup>¼</sup> tan �<sup>1</sup> sin <sup>Δ</sup>*<sup>φ</sup>*

the use of the linear combination of GPS measurements.

**3. Pseudorange multipath error mitigation**

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

by Eq. (3), (4), and (5).

*mφL*2= multipath error on *φ<sup>L</sup>*<sup>2</sup> [m].

**65**

The above explained method involves tedious trigonometric relationship and is difficult to determine the error due to multipath. To mitigate the multipath error, highly sensitive GPS receivers utilize multiple narrow-spaced correlators. But most of the multipath mitigation techniques related to GPS receiver hardware are not cost effective and need complex hardware to implement; whereas the data processing methods to mitigate the multipath error are more effective. These methods involve correction of GPS code and carrier phase measurements. Multipath elimination delay lock loop are used to mitigate multipath at the receiver signal processing level [27]. Most modern GPS receivers now employ similar algorithms. However, multipath cannot be completely removed and the residuals may still be too large to ignore when high accuracy positioning results are required. The antenna based mitigation techniques involve the use of antenna with a high sensitivity to right-hand circular polarized (RHCP) signals, choke-ring-ground-plane antenna and antenna arrays. The reflected signals typically contain a large LHCP component. Multipath susceptibility of an antenna can be quantified with respect to the antenna's gain pattern characteristics by the multipath ratio (MRP) [28]. Most of the multipath error modeling or mitigation methods are complex to implement; hence in the following section, a multipath mitigation technique on pseudorange on L1 carrier is given, which makes

Among the many errors affecting the GPS measurements [29], the predominant errors like ionospheric delay, multipath error and integer ambiguity are considered in the method. The range measurements on L1 and L2 carrier frequency are given

where *PL*<sup>1</sup> = pseudorange on L1 frequency [m]; *ρ* = geometric range [m]; *IL*<sup>1</sup> = ionospheric delay on L1 frequency [m]; *MPL*<sup>1</sup> = multipath error on *PL*<sup>1</sup> [m].

where *φ<sup>L</sup>*<sup>1</sup> = Carrier phase measurement on L1 frequency [m]; *φL*2= Carrier phase measurement on L2 frequency [m]; *NL*1, *NL*2= Integer ambiguity on L1 and

The multipath error in carrier phase measurements (*mφ<sup>L</sup>*<sup>1</sup> and *mφL*2) are assumed to be negligible compared to the error in pseudorange measurement. The expression for *MPL*<sup>1</sup> can be obtained by forming the appropriate linear combination of code range and carrier phase measurements (subtract Eq. (4) from Eq. (3)).

> *PL*<sup>1</sup> � *φ<sup>L</sup>*<sup>1</sup> ¼ 2*IL*<sup>1</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup> þ *MPL*<sup>1</sup> *PL1* � *φL1* � *2IL1* ¼ *MPL1* � *λL1NL1*

L2 frequencies respectively; *λL*1=Wavelength of L1 carrier frequency [m]; *λL*2=Wavelength of L2 carrier frequency [m]; *mφ<sup>L</sup>*<sup>1</sup> = Multipath error on *φL*1[m];

*PL*<sup>1</sup> ¼ *ρ* þ *IL*<sup>1</sup> þ *MPL*<sup>1</sup> (3)

*φ<sup>L</sup>*<sup>1</sup> ¼ *ρ* � *IL*<sup>1</sup> þ *λ<sup>L</sup>*1*NL*<sup>1</sup> þ *mφ<sup>L</sup>*<sup>1</sup> (4) *φ<sup>L</sup>*<sup>2</sup> ¼ *ρ* � *IL*<sup>2</sup> þ *λ<sup>L</sup>*2*NL*<sup>2</sup> þ *mφ<sup>L</sup>*<sup>2</sup> (5)

(6)

*α*�<sup>1</sup> þ cos Δ*φ*

(2)

#### **Figure 1.**

*GPS receiver block diagram showing extraction of code and carrier phase measurements from received composite signal.*

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

the correlation between the incoming code and an early version of the locally generated code. The Late correlator value is the correlation value between the incoming code and a late version of the locally generated code. In the presence of multipath, the time delay is estimated by correlating the composite signal with locally generate code(s), which result in code measurement errors. The impact of multipath on code phase measurements can be up to half a chip equivalent to a

In a GPS receiver, the carrier phase is measured by accumulating the phase of the numerically controlled oscillator (NCO) output. In an environment, where there are no reflected signals, the incoming signal carrier is the same as the direct signal carrier. The NCO generated local carrier locks onto the direct carrier very accurately, and, as a result, the true phase difference between the incoming signal carrier and the locally generated carrier is nearly zero, (actually zero mean), at steady state. The resulting phase measurements are very accurate. In the presence of multipath, however, the composite signal phase shifts from the direct signal phase, and the NCO-generated local carrier locks onto the composite carrier phase, resulting in an error in the phase measurement. The processing of the received

The change in carrier phase due to multipath effect can be determined in the PLL section of the GPS receiver as shown in **Figure 1**. Following steps are carried

Step 1: The direct signal received at the receiver is *Acosφ*. Here the "*A"* is

*GPS receiver block diagram showing extraction of code and carrier phase measurements from received composite*

*A* cos *φ* þ *α*ð Þ *A* cos *φ* þ Δ*φ* (1)

Step 2: The reflected signal is modeled as *α*ð Þ *Acosφ* þ Δ*φ* , where "*α*" is attenuation constant and "Δ*φ*" is change in carrier phase of the maximum

Step 3: The composite signal received as the GPS receiver is

range error of about 150 m for the GPS C/A code.

*Satellites Missions and Technologies for Geosciences*

signal in GPS receiver is shown below.

out to extract the error in phase due to multipath,

amplitude and "*φ*" is phase angle.

reflected signal.

**Figure 1.**

*signal.*

**64**

Step 4: The error in phase measurement due to multipath is modeled as,

$$\delta\rho = \tan^{-1}\left(\frac{\sin\,\Delta\rho}{a^{-1} + \cos\,\Delta\rho}\right) \tag{2}$$

The above explained method involves tedious trigonometric relationship and is difficult to determine the error due to multipath. To mitigate the multipath error, highly sensitive GPS receivers utilize multiple narrow-spaced correlators. But most of the multipath mitigation techniques related to GPS receiver hardware are not cost effective and need complex hardware to implement; whereas the data processing methods to mitigate the multipath error are more effective. These methods involve correction of GPS code and carrier phase measurements. Multipath elimination delay lock loop are used to mitigate multipath at the receiver signal processing level [27]. Most modern GPS receivers now employ similar algorithms. However, multipath cannot be completely removed and the residuals may still be too large to ignore when high accuracy positioning results are required. The antenna based mitigation techniques involve the use of antenna with a high sensitivity to right-hand circular polarized (RHCP) signals, choke-ring-ground-plane antenna and antenna arrays. The reflected signals typically contain a large LHCP component. Multipath susceptibility of an antenna can be quantified with respect to the antenna's gain pattern characteristics by the multipath ratio (MRP) [28]. Most of the multipath error modeling or mitigation methods are complex to implement; hence in the following section, a multipath mitigation technique on pseudorange on L1 carrier is given, which makes the use of the linear combination of GPS measurements.

#### **3. Pseudorange multipath error mitigation**

Among the many errors affecting the GPS measurements [29], the predominant errors like ionospheric delay, multipath error and integer ambiguity are considered in the method. The range measurements on L1 and L2 carrier frequency are given by Eq. (3), (4), and (5).

$$P\_{L1} = \rho + I\_{L1} + MP\_{L1} \tag{3}$$

where *PL*<sup>1</sup> = pseudorange on L1 frequency [m]; *ρ* = geometric range [m]; *IL*<sup>1</sup> = ionospheric delay on L1 frequency [m]; *MPL*<sup>1</sup> = multipath error on *PL*<sup>1</sup> [m].

$$
\rho\_{L1} = \rho - I\_{L1} + \lambda\_{L1} \mathbf{N}\_{L1} + m\rho\_{L1} \tag{4}
$$

$$
\rho\_{\rm L2} = \rho - I\_{\rm L2} + \lambda\_{\rm L2} N\_{\rm L2} + m \rho\_{\rm L2} \tag{5}
$$

where *φ<sup>L</sup>*<sup>1</sup> = Carrier phase measurement on L1 frequency [m]; *φL*2= Carrier phase measurement on L2 frequency [m]; *NL*1, *NL*2= Integer ambiguity on L1 and L2 frequencies respectively; *λL*1=Wavelength of L1 carrier frequency [m]; *λL*2=Wavelength of L2 carrier frequency [m]; *mφ<sup>L</sup>*<sup>1</sup> = Multipath error on *φL*1[m]; *mφL*2= multipath error on *φ<sup>L</sup>*<sup>2</sup> [m].

The multipath error in carrier phase measurements (*mφ<sup>L</sup>*<sup>1</sup> and *mφL*2) are assumed to be negligible compared to the error in pseudorange measurement. The expression for *MPL*<sup>1</sup> can be obtained by forming the appropriate linear combination of code range and carrier phase measurements (subtract Eq. (4) from Eq. (3)).

$$\begin{aligned} P\_{L1} - \varrho\_{L1} &= 2I\_{L1} - \lambda\_{L1} N\_{L1} + MP\_{L1} \\ P\_{L1} - \varrho\_{L1} - 2I\_{L1} &= MP\_{L1} - \lambda\_{L1} N\_{L1} \end{aligned} \tag{6}$$

To make the above equation free from ionospheric delay, the Eq. (5) is subtracted from Eq. (4) and rearranged as below,

$$
\rho\_{L1} - \rho\_{L2} = I\_{L2} - I\_{L1} + \lambda\_{L1} \mathbf{N}\_{L1} - \lambda\_{L2} \mathbf{N}\_{L2} \tag{7}
$$

Since the ionosphere is dispersive medium, the delay is frequency dependent. Hence the delays (*IL*<sup>1</sup> and *IL*2) are related to the respective carrier frequencies ( *f <sup>L</sup>*<sup>1</sup> and *f <sup>L</sup>*2) as,

$$\left(f\_{L1}/f\_{L2}\right)^2 = \mathbf{I}\_{L2}/\mathbf{I}\_{L1} \tag{8}$$

**4. Results and discussion**

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

(Lat: 17.73<sup>o</sup>

visible satellites.

**Table 2.**

**67**

Statistical analysis of the results shows that multipath error is too large to neglect. These errors are estimated for location having ECEF coordinates as xu = 706970.90 m, yu = 6035941.02 m, and zu = 1930009.58 m in the Indian subcontinent for typical ephemerides data collected on March 11, 2019, from the dual frequency GPS receiver located at Department of Electronics and Communication Engineering, Andhra University College of Engineering, Visakhapatnam

11,161 sq.kms is surrounded by Eastern Ghat Range, viz. Kailasa, Yarada and Narava hill ranges on north, south and west, respectively, and Bay of Bengal in the east. Due to this geography the GPS signals bound to get reflected. The data are collected on March 11, 2019, for entire 24 hrs with an epoch interval of 30 s. On March 11, 2019, the global geomagnetic activity index, i.e., Kp-index was 3. The average level for geomagnetic activity, i.e., Ap index was 2 and the noise level generated by the sun at a wavelength of 10.7 cm at the earth's orbit, i.e., solar index F10.7 was 69.5. These indices imply that on this particular day there was no solar storm or geomagnetic storm and the solar activity was normal. The solar activity affects the ionization in ionosphere and hence the signal propagation through this layer. The expression derived above for multipath error is ionospheric delay free. Hence the estimated multipath error is unaffected by ionospheric delay. For the observation period of 24 h, error analysis which is supported by the relevant graphs and tables are presented in this chapter. During this observation period, out of 32 satellites, minimum of 9 satellites were visible in each epoch. Though the error is computed and analyzed for all the visible satellites, the multipath error estimated for SV PRN07, 23, 28 and 31 are presented in this chapter. Navigation solution for each epoch is calculated using pseudoranges (multipath error corrected) of all the

N/Long: 83.319°E), India. The city of Visakhapatnam with an area of

**Table 2** illustrates the multipath error for four satellites. Similar results were also obtained for all the visible satellites. Table also details the error in receiver position distance from the surveyed location. **Figure 2** shows the trajectories of the satellites 07, 23, 28 and 31 with respect to elevation and azimuth angles. The subplots of **Figure 3** show the change in multipath error with respect to change in elevation angle. **Figure 2** shows that the satellites were visible to the receiver at low elevation angle and rose to highest elevation angle of 70°, 84°, 52° and 82°, respectively. The receiver continued tracking the satellites. The satellites went out of the sight of the receiver when they set with low elevation angle. In each of the subplots **Figure 3** two curves of the change in multipath against the elevation angle are shown. One curve indicates the multipath error while the satellite was rising and the other when it was setting after reaching the highest elevation angle. From **Figure 3(a)**–**(d)**, it is

**Pseudorange multipath error on L1 frequency[m] Error in receiver**

**distance [m]** *SV PRN07 SV PRN23 SV PRN28 SV PRN31*

Min 7.362 14.11 40.21 9.136 �25.8 Max 14.32 18.79 52.88 13.52 31.49 Standard deviation 1.816 1.439 1.984 1.019 10.78

*Pseudorange multipath error for satellites signal on L1 frequency.*

**position**

By substituting the above equation for *IL2* in terms of *IL1*, we get

$$\begin{aligned} \rho\_{L1} - \rho\_{L2} &= \left(f\_{L1}/f\_{L2}\right)^2 \times I\_{L1} - I\_{L1} + \lambda\_{L1}N\_{L1} - \lambda\_{L2}N\_{L2} \\\\ \rho\_{L1} - \rho\_{L2} &= \left(\left(f\_{L1}/f\_{L2}\right)^2 - 1\right) \times I\_{L1} + \lambda\_{L1}N\_{L1} - \lambda\_{L2}N\_{L2} \end{aligned} \tag{9}$$

Simplifying Eq. (9) we get,

$$I\_{L1} = \mathbf{1} / \left( \left( f\_{L1} / f\_{L2} \right)^2 - \mathbf{1} \right) \times \left( \rho\_{L1} - \rho\_{L2} \right) + \mathbf{1} / \left( \left( f\_{L1} / f\_{L2} \right)^2 - \mathbf{1} \right) \times \left( \lambda\_{L2} N\_{L2} - \lambda\_{L1} N\_{L1} \right) \tag{10}$$

Substituting the above expression for *I*<sup>1</sup> in Eq. (6) we get,

$$\begin{split} \mathbf{MP}\_{L1} - \lambda\_{L1} \mathbf{N}\_{L1} &= \mathbf{P}\_{L1} - \boldsymbol{\varrho}\_{L1} - 2 \left( \left( \boldsymbol{f}\_{L1}/\boldsymbol{f}\_{L2} \right)^{2} - \mathbf{1} \right) \times \left( \boldsymbol{\varrho}\_{L1} - \boldsymbol{\varrho}\_{L2} \right) \\ &+ 2 \left( \left( \boldsymbol{f}\_{L1}/\boldsymbol{f}\_{L2} \right)^{2} - \mathbf{1} \right) \times \left( \lambda\_{L2} \mathbf{N}\_{L2} - \lambda\_{L1} \mathbf{N}\_{L1} \right) \end{split} \tag{11}$$

Rearranging the terms in Eq. (11) we get,

$$\begin{aligned} \text{MP}\_{L1} &- \left(\lambda\_{L1}\text{N}\_{L1} - 2 / \left(\left(f\_{L1}/f\_{L2}\right)^2 - 1\right) \times \left(\lambda\_{L2}\text{N}\_{L2} - \lambda\_{L1}\text{N}\_{L1}\right)\right) \\ &= P\_{L1} - \left(\left(f\_{L1}/f\_{L2}\right)^2 + 1\right) / \left(\left(f\_{L1}/f\_{L2}\right)^2 - 1\right) \times q\_{L1} + 2 / \left(\left(f\_{L1}/f\_{L2}\right)^2 - 1\right) \times q\_{L2} \end{aligned} \tag{12}$$

$$\begin{aligned} \text{MP}\_{L1} &= \left(\lambda\_{L1}\text{N}\_{L1} - 2/\left(\left(f\_{L1}/f\_{L2}\right)^2 - \mathbf{1}\right) \times \left(\lambda\_{L2}\text{N}\_{L2} - \lambda\_{L1}\text{N}\_{L1}\right)\right) \text{P}\_{L1} \\ &- \left(\left(f\_{L1}/f\_{L2}\right)^2 + \mathbf{1}\right) / \left(\left(f\_{L1}/f\_{L2}\right)^2 - \mathbf{1}\right) \times \boldsymbol{\varrho}\_{L1} + \mathbf{2} / \left(\left(f\_{L1}/f\_{L2}\right)^2 - \mathbf{1}\right) \times \boldsymbol{\varrho}\_{L2} \end{aligned} \tag{13}$$

In above equation *λ<sup>L</sup>*1*NL*<sup>1</sup> � 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup> <sup>2</sup> � <sup>1</sup> � ð Þ *<sup>λ</sup><sup>L</sup>*2*NL*<sup>2</sup> � *<sup>λ</sup><sup>L</sup>*1*NL*<sup>1</sup> is constant and expectation of *MPL1* is assumed as zero. The impact of the multipath error and its variation with respect to elevation angle of the satellites for the entire duration of observation are analyzed. This analysis will be helpful in kinematic applications where multipath signal becomes more arbitrary, particularly in aircraft navigation and missile guidance where the reflecting geometry and the environment around the receiving antenna changes relatively in random way [29].

#### **4. Results and discussion**

To make the above equation free from ionospheric delay, the Eq. (5) is

*f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

By substituting the above equation for *IL2* in terms of *IL1*, we get

 <sup>2</sup> � <sup>1</sup> 

� *φ<sup>L</sup>*<sup>1</sup> � *φ<sup>L</sup>*<sup>2</sup> ð Þþ 1*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

<sup>2</sup>

Substituting the above expression for *I*<sup>1</sup> in Eq. (6) we get,

þ 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

 <sup>2</sup> � <sup>1</sup> 

 <sup>2</sup> � <sup>1</sup> 

*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup> <sup>2</sup> � <sup>1</sup> 

*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup> <sup>2</sup> � <sup>1</sup> 

 <sup>2</sup> � <sup>1</sup> 

*MPL*<sup>1</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup> ¼ *PL*<sup>1</sup> � *φ<sup>L</sup>*<sup>1</sup> � 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

Rearranging the terms in Eq. (11) we get,

Since the ionosphere is dispersive medium, the delay is frequency dependent. Hence the delays (*IL*<sup>1</sup> and *IL*2) are related to the respective carrier frequencies ( *f <sup>L</sup>*<sup>1</sup>

*φL*<sup>1</sup> � *φL*<sup>2</sup> ¼ *IL*<sup>2</sup> � *IL*<sup>1</sup> þ *λL*1*NL*<sup>1</sup> � *λL*2*NL*<sup>2</sup> (7)

<sup>2</sup> <sup>¼</sup> *IL*2*=IL*<sup>1</sup> (8)

� *IL*<sup>1</sup> þ *λ<sup>L</sup>*1*NL*<sup>1</sup> � *λ<sup>L</sup>*2*NL*<sup>2</sup>

� *φ<sup>L</sup>*<sup>1</sup> � *φ<sup>L</sup>*<sup>2</sup> ð Þ

� ð Þ *λ<sup>L</sup>*2*NL*<sup>2</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup> (11)

 <sup>2</sup> � <sup>1</sup> 

 <sup>2</sup> � <sup>1</sup> 

� *φ<sup>L</sup>*<sup>1</sup> þ 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

� *φ<sup>L</sup>*<sup>1</sup> þ 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

*PL*<sup>1</sup>

� ð Þ *λ<sup>L</sup>*2*NL*<sup>2</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup>

(9)

(10)

� *φ<sup>L</sup>*<sup>2</sup> (12)

� *φ<sup>L</sup>*<sup>2</sup> (13)

is con-

� ð Þ *λ<sup>L</sup>*2*NL*<sup>2</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup>

� *IL*<sup>1</sup> � *IL*<sup>1</sup> þ *λ<sup>L</sup>*1*NL*<sup>1</sup> � *λ<sup>L</sup>*2*NL*<sup>2</sup>

 <sup>2</sup> � <sup>1</sup> 

 <sup>2</sup> � <sup>1</sup> 

� ð Þ *λ<sup>L</sup>*2*NL*<sup>2</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup>

� ð Þ *λ<sup>L</sup>*2*NL*<sup>2</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup>

 <sup>2</sup> � <sup>1</sup> 

stant and expectation of *MPL1* is assumed as zero. The impact of the multipath error and its variation with respect to elevation angle of the satellites for the entire duration of observation are analyzed. This analysis will be helpful in kinematic applications where multipath signal becomes more arbitrary, particularly in aircraft navigation and missile guidance where the reflecting geometry and the environment around the receiving antenna changes relatively in random way [29].

subtracted from Eq. (4) and rearranged as below,

*Satellites Missions and Technologies for Geosciences*

*φ<sup>L</sup>*<sup>1</sup> � *φ<sup>L</sup>*<sup>2</sup> ¼ *f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

*φ<sup>L</sup>*<sup>1</sup> � *φ<sup>L</sup>*<sup>2</sup> ¼ *f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

Simplifying Eq. (9) we get,

 <sup>2</sup> � <sup>1</sup> 

*MPL*<sup>1</sup> � *λ<sup>L</sup>*1*NL*<sup>1</sup> � 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

*MPL*<sup>1</sup> ¼ *λ<sup>L</sup>*1*NL*<sup>1</sup> � 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

� *f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

 <sup>2</sup> <sup>þ</sup> <sup>1</sup> 

 <sup>2</sup> <sup>þ</sup> <sup>1</sup> 

In above equation *λ<sup>L</sup>*1*NL*<sup>1</sup> � 2*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

¼ *PL*<sup>1</sup> � *f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

**66**

*IL*<sup>1</sup> ¼ 1*= f <sup>L</sup>*1*= f <sup>L</sup>*<sup>2</sup>

and *f <sup>L</sup>*2) as,

Statistical analysis of the results shows that multipath error is too large to neglect. These errors are estimated for location having ECEF coordinates as xu = 706970.90 m, yu = 6035941.02 m, and zu = 1930009.58 m in the Indian subcontinent for typical ephemerides data collected on March 11, 2019, from the dual frequency GPS receiver located at Department of Electronics and Communication Engineering, Andhra University College of Engineering, Visakhapatnam (Lat: 17.73<sup>o</sup> N/Long: 83.319°E), India. The city of Visakhapatnam with an area of 11,161 sq.kms is surrounded by Eastern Ghat Range, viz. Kailasa, Yarada and Narava hill ranges on north, south and west, respectively, and Bay of Bengal in the east. Due to this geography the GPS signals bound to get reflected. The data are collected on March 11, 2019, for entire 24 hrs with an epoch interval of 30 s. On March 11, 2019, the global geomagnetic activity index, i.e., Kp-index was 3. The average level for geomagnetic activity, i.e., Ap index was 2 and the noise level generated by the sun at a wavelength of 10.7 cm at the earth's orbit, i.e., solar index F10.7 was 69.5. These indices imply that on this particular day there was no solar storm or geomagnetic storm and the solar activity was normal. The solar activity affects the ionization in ionosphere and hence the signal propagation through this layer. The expression derived above for multipath error is ionospheric delay free. Hence the estimated multipath error is unaffected by ionospheric delay. For the observation period of 24 h, error analysis which is supported by the relevant graphs and tables are presented in this chapter. During this observation period, out of 32 satellites, minimum of 9 satellites were visible in each epoch. Though the error is computed and analyzed for all the visible satellites, the multipath error estimated for SV PRN07, 23, 28 and 31 are presented in this chapter. Navigation solution for each epoch is calculated using pseudoranges (multipath error corrected) of all the visible satellites.

**Table 2** illustrates the multipath error for four satellites. Similar results were also obtained for all the visible satellites. Table also details the error in receiver position distance from the surveyed location. **Figure 2** shows the trajectories of the satellites 07, 23, 28 and 31 with respect to elevation and azimuth angles. The subplots of **Figure 3** show the change in multipath error with respect to change in elevation angle. **Figure 2** shows that the satellites were visible to the receiver at low elevation angle and rose to highest elevation angle of 70°, 84°, 52° and 82°, respectively. The receiver continued tracking the satellites. The satellites went out of the sight of the receiver when they set with low elevation angle. In each of the subplots **Figure 3** two curves of the change in multipath against the elevation angle are shown. One curve indicates the multipath error while the satellite was rising and the other when it was setting after reaching the highest elevation angle. From **Figure 3(a)**–**(d)**, it is


#### **Table 2.**

*Pseudorange multipath error for satellites signal on L1 frequency.*

**Figure 2.**

*Sky plot for the mentioned satellite orbits as viewed from GPS receiver located at Department of ECE, Andhra University (Lat: 17.73<sup>o</sup> N, Long: 83.31°E).*

**5. Conclusions**

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

**Figure 4.**

*11, 2019.*

tracking.

**69**

The statistics and result analysis comprises of investigation of error magnitude variations over a period of 24 h. Signals transmitted from the satellites, visible at low elevation angles, travel a longer path through the propagation medium and are subjected to multiple reflections than the satellites at higher elevation angle. From the results, the maximum multipath error of 52 m is observed for SVPRN28 at an elevation angle of 8° and minimum error of 7 m is observed for SVPRN7 at an elevation angle of 70°. But for SVPRN23, the minimum error is 14 m even though the elevation angle of the satellite is 84°. This is due to the multiple reflections the signal underwent for that particular azimuth angle, which determines the direction of the signal. The GPS receiver location is surrounded by high hill ranges and large

*Error in distance of GPS receiver position from surveyed position over the observation period of 24 h on March*

water body, this would have led to multiple reflections and hence the large

multipath error in spite of high elevation angle of the satellite. The receiver position error in distance with respect to actual position is 30 m. This is due to the residual errors in the pseudorange measurement as the errors other than multipath remain uncorrected. Along with the multipath error mitigation technique mentioned in this chapter, if other errors are also corrected the receiver position will be more accurate. The proposed algorithm to estimate multipath error is essential for all precise navigation applications (e.g., CAT I/II aircraft landings, missile navigation) and especially in surveying applications in urban canyon. The impact analysis done in this chapter will also be a valuable aid in selecting a location to set up the GPS receiver antenna with least multipath error for surveying, aircraft navigation and

**Figure 3.**

*(a)–(d) Pseudorange multipath error for respective satellites against the elevation angle for the observation period of 24 h.*

observed that for the elevation angle of less than 10°, the multipath errors are 14.32 m, 18.79 m, 52.88 m and 13.52 m respectively. **Figure 4** shows the receiver position error in distance with respect to actual ECEF coordinates of the receiver. The figure shows the maximum error of 30 m. This is due to the residual errors in the pseudorange measurement. Though the pseudoranges of all the visible satellites are corrected for multipath error but the errors other than multipath remain uncorrected. The standard deviation of position error in distance is 10.78 m.

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

**Figure 4.**

*Error in distance of GPS receiver position from surveyed position over the observation period of 24 h on March 11, 2019.*

#### **5. Conclusions**

The statistics and result analysis comprises of investigation of error magnitude variations over a period of 24 h. Signals transmitted from the satellites, visible at low elevation angles, travel a longer path through the propagation medium and are subjected to multiple reflections than the satellites at higher elevation angle. From the results, the maximum multipath error of 52 m is observed for SVPRN28 at an elevation angle of 8° and minimum error of 7 m is observed for SVPRN7 at an elevation angle of 70°. But for SVPRN23, the minimum error is 14 m even though the elevation angle of the satellite is 84°. This is due to the multiple reflections the signal underwent for that particular azimuth angle, which determines the direction of the signal. The GPS receiver location is surrounded by high hill ranges and large water body, this would have led to multiple reflections and hence the large multipath error in spite of high elevation angle of the satellite. The receiver position error in distance with respect to actual position is 30 m. This is due to the residual errors in the pseudorange measurement as the errors other than multipath remain uncorrected. Along with the multipath error mitigation technique mentioned in this chapter, if other errors are also corrected the receiver position will be more accurate. The proposed algorithm to estimate multipath error is essential for all precise navigation applications (e.g., CAT I/II aircraft landings, missile navigation) and especially in surveying applications in urban canyon. The impact analysis done in this chapter will also be a valuable aid in selecting a location to set up the GPS receiver antenna with least multipath error for surveying, aircraft navigation and tracking.

observed that for the elevation angle of less than 10°, the multipath errors are 14.32 m, 18.79 m, 52.88 m and 13.52 m respectively. **Figure 4** shows the receiver position error in distance with respect to actual ECEF coordinates of the receiver. The figure shows the maximum error of 30 m. This is due to the residual errors in the pseudorange measurement. Though the pseudoranges of all the visible satellites are corrected for multipath error but the errors other than multipath remain uncorrected. The standard deviation of position error in distance is 10.78 m.

*(a)–(d) Pseudorange multipath error for respective satellites against the elevation angle for the observation*

*Sky plot for the mentioned satellite orbits as viewed from GPS receiver located at Department of ECE, Andhra*

**Figure 2.**

**Figure 3.**

**68**

*period of 24 h.*

*University (Lat: 17.73<sup>o</sup>*

*N, Long: 83.31°E).*

*Satellites Missions and Technologies for Geosciences*

*Satellites Missions and Technologies for Geosciences*

**References**

978-81-322-2728-1

Cambridge Press; 1997

[1] Bidikar B, Sasibhushana Rao G, Ganesh L, Santosh Kumar MNVS. GPS C/A code multipath error estimation for surveying applications in urban canyon. In: Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering. Vol. 372. Springer. 2016. pp. 135-142. ISBN:

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

environment. In: ITSNT 2018,

itsnt2018.22.hal-01890371f

International Technical Symposium on Navigation and Timing; October, Toulosue, France. 2018. DOI: 10.31701/

[9] Clare A, Lin T, Lachapelle G. Effect of GNSS receiver carrier phase tracking

[10] Cheng L, Chen J, Gan M. Multipath error analysis of carrier tracking loop in GPS receiver. In: Proceedings of the 29th Chinese Control Conference; Beijing. 2010. pp. 4137-4141

Benhallam A, Chatre E. Performance of GPS receivers with more than one multipath. In: ION GPS 1999, 12th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, United States. 1999. pp. 281-288. ffhal-01021687f

[12] Cui J, Kouguchi N. Ocean wave observation by GPS signal. In: OCEANS 2011 IEEE; Spain, Santander. 2011. pp. 1-7

[13] Avram A, Schwieger V, El Gemayel N. Experimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios. Journal of Applied Geodesy.

[14] Falkenberg W, Kielland P, Lachapelle G. GPS differential positioning technologies for

[15] Lachapelle G, Falkenberg W, Neufeldt D, Keilland P. Marine DGPS using code and carrier in multipath environment. In: Proceedings of ION GPS-89, Colorado, Springs, September

27–29. 1989. pp. 343-347

hydrographic surveying. In: Proceedings of IEEE PLANS, Orlando, December.

2019;**13**(4):279-289

1988. pp. 310-317

loops on earthquake monitoring performance. Advances in Space Research. 2017;**59**(11):2740-2749

[11] Macabiau C, Roturier B,

[2] Sunehra D. Estimation of prominent global positioning system measurement errors for Gagan applications. European Scientific Journal. 2013;**9**(15). ISSN: 1857-7881 (Print) e-ISSN: 1857-7431 68

[3] Borre K, Strang G. Linear Algebra Geodesy and GPS. USA: Wellesley-

[4] Demyanov VV, Sergeeva MA, Yasyukevich AS. GNSS high-rate data

scintillation indices. In: Demyanov V, editor. Ionospheric and Atmospheric Threats for GNSS and Satellite Telecommunications. Croatia. ISBN: 978-1-78985-996-6.: IntechOpen; 2019. DOI: 10.5772/intechopen.90078

[5] Nahavandchi H et al. Correlation analysis of multipath effects in GPScode and carrier phase observations. Survey Review. 2010;**42**(316):193-206

[6] Shkarofsky IP et al. Multipath depolarization theory combining antenna with atmospheric and ground

reflection effects. Annales des Télécommunications. 1981;**36**(1-2):

[7] Jayanta KR. Mitigation of GPS code and carrier phase multipath effects using a multi-antenna system [Thesis]. Calgary, Alberta: The University of

[8] Matera ER, Peña AJG, Julien O, Ekambi B. Characterization of pseudo range multipath errors in an urban

83-88

**71**

Calgary; 2000

and efficiency of ionospheric

### **Author details**

Bharati Bidikar, Babji Prasad Chapa, Mogadala Vinod Kumar and Gottapu Sasibhushana Rao\* Department of Electronics and Communications Engineering, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, India

\*Address all correspondence to: sasigps@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*GPS Signal Multipath Error Mitigation Technique DOI: http://dx.doi.org/10.5772/intechopen.92295*

#### **References**

[1] Bidikar B, Sasibhushana Rao G, Ganesh L, Santosh Kumar MNVS. GPS C/A code multipath error estimation for surveying applications in urban canyon. In: Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering. Vol. 372. Springer. 2016. pp. 135-142. ISBN: 978-81-322-2728-1

[2] Sunehra D. Estimation of prominent global positioning system measurement errors for Gagan applications. European Scientific Journal. 2013;**9**(15). ISSN: 1857-7881 (Print) e-ISSN: 1857-7431 68

[3] Borre K, Strang G. Linear Algebra Geodesy and GPS. USA: Wellesley-Cambridge Press; 1997

[4] Demyanov VV, Sergeeva MA, Yasyukevich AS. GNSS high-rate data and efficiency of ionospheric scintillation indices. In: Demyanov V, editor. Ionospheric and Atmospheric Threats for GNSS and Satellite Telecommunications. Croatia. ISBN: 978-1-78985-996-6.: IntechOpen; 2019. DOI: 10.5772/intechopen.90078

[5] Nahavandchi H et al. Correlation analysis of multipath effects in GPScode and carrier phase observations. Survey Review. 2010;**42**(316):193-206

[6] Shkarofsky IP et al. Multipath depolarization theory combining antenna with atmospheric and ground reflection effects. Annales des Télécommunications. 1981;**36**(1-2): 83-88

[7] Jayanta KR. Mitigation of GPS code and carrier phase multipath effects using a multi-antenna system [Thesis]. Calgary, Alberta: The University of Calgary; 2000

[8] Matera ER, Peña AJG, Julien O, Ekambi B. Characterization of pseudo range multipath errors in an urban

environment. In: ITSNT 2018, International Technical Symposium on Navigation and Timing; October, Toulosue, France. 2018. DOI: 10.31701/ itsnt2018.22.hal-01890371f

[9] Clare A, Lin T, Lachapelle G. Effect of GNSS receiver carrier phase tracking loops on earthquake monitoring performance. Advances in Space Research. 2017;**59**(11):2740-2749

[10] Cheng L, Chen J, Gan M. Multipath error analysis of carrier tracking loop in GPS receiver. In: Proceedings of the 29th Chinese Control Conference; Beijing. 2010. pp. 4137-4141

[11] Macabiau C, Roturier B, Benhallam A, Chatre E. Performance of GPS receivers with more than one multipath. In: ION GPS 1999, 12th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, United States. 1999. pp. 281-288. ffhal-01021687f

[12] Cui J, Kouguchi N. Ocean wave observation by GPS signal. In: OCEANS 2011 IEEE; Spain, Santander. 2011. pp. 1-7

[13] Avram A, Schwieger V, El Gemayel N. Experimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios. Journal of Applied Geodesy. 2019;**13**(4):279-289

[14] Falkenberg W, Kielland P, Lachapelle G. GPS differential positioning technologies for hydrographic surveying. In: Proceedings of IEEE PLANS, Orlando, December. 1988. pp. 310-317

[15] Lachapelle G, Falkenberg W, Neufeldt D, Keilland P. Marine DGPS using code and carrier in multipath environment. In: Proceedings of ION GPS-89, Colorado, Springs, September 27–29. 1989. pp. 343-347

**Author details**

**70**

and Gottapu Sasibhushana Rao\*

*Satellites Missions and Technologies for Geosciences*

Bharati Bidikar, Babji Prasad Chapa, Mogadala Vinod Kumar

\*Address all correspondence to: sasigps@gmail.com

provided the original work is properly cited.

Department of Electronics and Communications Engineering, Andhra University

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

College of Engineering (A), Andhra University, Visakhapatnam, India

[16] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/ s00190-018-1172-9

[17] Axelrad P, Comp CJ, MacDoran PF. Use of signal-to-noise ratio for multipath error correction in GPS differential phase measurements. In: Proceedings of ION GPS-94; 20–23 September; Salt Lake City, USA. 1994. pp. 655-666

[18] Xia L, Liu J. Approach for multipath reduction using wavelet algorithm. In: Proceedings of ION GPS 2001; 11–14 September; Salt Lake City, USA. 2001. pp. 2134-2143

[19] de Souza EM, Monico JFG. Wavelet shrinkage: High frequency multipath reduction from GPS relative positioning. GPS Solutions. 2004;**8**:152-159

[20] Satirapod C, Ogaja C, Wang J, Rizos C. An approach to GPS analysis incorporating wavelet decomposition. Artificial Satellites. 2001;**36**:27-35

[21] Zheng DW, Zhong P, Ding XL, Chen W. Filtering GPS time-series using a Vondrak filter and cross-validation. Journal of Geodesy. 2005;**79**:363-369

[22] Ge L, Han S, Rizos C. Multipath mitigation of continuous GPS measurements using an adaptive filter. GPS Solutions. 2000;**4**:19-30

[23] Xie P et al. Measuring GNSS multipath distributions in urban canyon environments. IEEE Transactions on Instrumentation and Measurement. 2015;**64**(2)

[24] Kaplan ED. Understanding GPS: Principles and Applications. 2nd ed. Boston, USA: Artech House Publishers; 2006

[25] Townsend B, Fenton R. A practical approach to the reduction of pseudorange multipath errors in an L1 GPS receiver. In: 7th Intenational Technical Meeting of the Satellite Division of the U.S. Inst. of Navigation, 20–23 September; Salt Lake City, Utah. 1994. pp. 143-148

[26] Rao GS. Global Navigation Satellite Systems. 1st ed. India: McGraw-Hill; 2010

[27] Pratap M, Per E. Global Positioning System: Signals, Measurements and Performance. 2nd ed. New York: Ganga-Jamuna Press; 2006

[28] Parkinson BW, Spilker JR. Global Positioning System: Theory and Applications. Washington DC: American Institute of Aeronautics and Astronautics; 1996

[29] Happel DA. Use of military GPS in a civil environment. In: Proceedings of the 59th Annual Meeting of the Institute of Navigation and CIGTF 22nd Guidance Test Symposium; Albuquerque, NM. 2003. pp. 57-64

**73**

**Chapter 5**

**Abstract**

**1. Introduction**

The Impact of Space Radiation

Operation in Near-Earth Space

*Victor U.J. Nwankwo, Nnamdi N. Jibiri and Michael T. Kio*

Energetic particles and electromagnetic radiation (EM) from solar events and galactic cosmic rays can bombard and interact with satellites' exposed surfaces, and sometimes possess enough energy to penetrate their surface. Among other known effects, the scenario can cause accelerated orbit decay due to atmospheric drag, sporadic and unexplainable errors in functions of sensitive parts, degradation of critical properties of structural materials, jeopardy of flight worthiness, transient and terminal health hazard to both onboard passengers and astronauts, and sometimes a catastrophic failure that can abruptly end satellite mission. The understanding of the dynamics of the space radiation environment and associated effects is critically important for satellites design and operation in ionospheric plasma environment, in which satellites are designed to function. In this chapter we review some satellite anomalies associated with the space radiation environment and conclude with

Environment on Satellites

mitigation effort that can reduce such impact.

**Keywords:** solar activity, energetic particles, radiation environment,

Solar activity drives dynamic changes in the atmosphere and ionosphere that can affect the performance and reliability of satellites in near-Earth space environment, as well as ground-based technological systems and services that rely on them. This condition is referred to as space weather. The principal medium through which the Sun's activity is communicated to the region of the near-Earth space environment, is the solar wind, which occurs in form of a continuous outflow of streams of energized charged particles and/or momentary eruption of large-scale, high-mass plasma known as coronal mass ejections (CMEs). Sources of energised particles and strong magnetic energy also include the solar flares and galactic cosmic ray, originating from outer space. The energetic particles and electromagnetic radiation from these processes form the near-Earth radiation environment and can be divided into (i) trapped radiation environment and (ii) transient radiation environments. The charged particles that are trapped or confined by the Earth's magnetic field to certain regions in space such as the Van Allen belts form the trapped radiation environment. The transient particles environment consists of energetic particles from solar events, and galactic cosmic radiation that exist in the interplanetary space regions and in the near-Earth regions. Satellites and other space application systems are vulnerable to

single event effects, total ionizing dose, impact mitigation

#### **Chapter 5**

[16] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/

*Satellites Missions and Technologies for Geosciences*

[25] Townsend B, Fenton R. A practical approach to the reduction of pseudorange multipath errors in an L1 GPS receiver. In: 7th Intenational Technical Meeting of the Satellite Division of the

September; Salt Lake City, Utah. 1994.

[26] Rao GS. Global Navigation Satellite Systems. 1st ed. India: McGraw-Hill;

[27] Pratap M, Per E. Global Positioning System: Signals, Measurements and Performance. 2nd ed. New York: Ganga-

[28] Parkinson BW, Spilker JR. Global Positioning System: Theory and Applications. Washington DC:

American Institute of Aeronautics and

[29] Happel DA. Use of military GPS in a civil environment. In: Proceedings of the 59th Annual Meeting of the Institute

of Navigation and CIGTF 22nd Guidance Test Symposium;

Albuquerque, NM. 2003. pp. 57-64

U.S. Inst. of Navigation, 20–23

pp. 143-148

Jamuna Press; 2006

Astronautics; 1996

2010

[17] Axelrad P, Comp CJ, MacDoran PF.

[18] Xia L, Liu J. Approach for multipath reduction using wavelet algorithm. In: Proceedings of ION GPS 2001; 11–14 September; Salt Lake City, USA. 2001.

[19] de Souza EM, Monico JFG. Wavelet shrinkage: High frequency multipath reduction from GPS relative positioning.

GPS Solutions. 2004;**8**:152-159

[20] Satirapod C, Ogaja C, Wang J, Rizos C. An approach to GPS analysis incorporating wavelet decomposition. Artificial Satellites. 2001;**36**:27-35

[21] Zheng DW, Zhong P, Ding XL, Chen W. Filtering GPS time-series using a Vondrak filter and cross-validation. Journal of Geodesy. 2005;**79**:363-369

[22] Ge L, Han S, Rizos C. Multipath mitigation of continuous GPS

GPS Solutions. 2000;**4**:19-30

2015;**64**(2)

2006

**72**

[23] Xie P et al. Measuring GNSS

measurements using an adaptive filter.

multipath distributions in urban canyon environments. IEEE Transactions on Instrumentation and Measurement.

[24] Kaplan ED. Understanding GPS: Principles and Applications. 2nd ed. Boston, USA: Artech House Publishers;

Use of signal-to-noise ratio for multipath error correction in GPS differential phase measurements. In: Proceedings of ION GPS-94; 20–23 September; Salt Lake City, USA. 1994.

s00190-018-1172-9

pp. 655-666

pp. 2134-2143

## The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space

*Victor U.J. Nwankwo, Nnamdi N. Jibiri and Michael T. Kio*

#### **Abstract**

Energetic particles and electromagnetic radiation (EM) from solar events and galactic cosmic rays can bombard and interact with satellites' exposed surfaces, and sometimes possess enough energy to penetrate their surface. Among other known effects, the scenario can cause accelerated orbit decay due to atmospheric drag, sporadic and unexplainable errors in functions of sensitive parts, degradation of critical properties of structural materials, jeopardy of flight worthiness, transient and terminal health hazard to both onboard passengers and astronauts, and sometimes a catastrophic failure that can abruptly end satellite mission. The understanding of the dynamics of the space radiation environment and associated effects is critically important for satellites design and operation in ionospheric plasma environment, in which satellites are designed to function. In this chapter we review some satellite anomalies associated with the space radiation environment and conclude with mitigation effort that can reduce such impact.

**Keywords:** solar activity, energetic particles, radiation environment, single event effects, total ionizing dose, impact mitigation

#### **1. Introduction**

Solar activity drives dynamic changes in the atmosphere and ionosphere that can affect the performance and reliability of satellites in near-Earth space environment, as well as ground-based technological systems and services that rely on them. This condition is referred to as space weather. The principal medium through which the Sun's activity is communicated to the region of the near-Earth space environment, is the solar wind, which occurs in form of a continuous outflow of streams of energized charged particles and/or momentary eruption of large-scale, high-mass plasma known as coronal mass ejections (CMEs). Sources of energised particles and strong magnetic energy also include the solar flares and galactic cosmic ray, originating from outer space. The energetic particles and electromagnetic radiation from these processes form the near-Earth radiation environment and can be divided into (i) trapped radiation environment and (ii) transient radiation environments. The charged particles that are trapped or confined by the Earth's magnetic field to certain regions in space such as the Van Allen belts form the trapped radiation environment. The transient particles environment consists of energetic particles from solar events, and galactic cosmic radiation that exist in the interplanetary space regions and in the near-Earth regions. Satellites and other space application systems are vulnerable to

both trapped and transient energetic particles since they are basically designed to operate in the space plasma environment. The particles can bombard and interact with satellites' surfaces, and sometimes posses enough energy to penetrate their exposed surfaces with possible access to their electrical, electronic and electrochemical components (EEECs). This scenario can induce sporadic and unexplainable errors in sensitive parts of spacecrafts, degrade the critical properties of their structural materials, jeopardize the flight worthiness of spacecrafts, constitute transient and terminal health hazard to both onboard passengers and astronauts, and even lead to total failure that can end the mission of affected spacecrafts [1, 2].

There are documented cases or evidence of satellites anomaly associated with space weather (or space radiation environment). In their study, Iucci et al. [3] verified and quantified the linkage between a large fraction of spacecraft anomalies and space weather perturbations. They compiled a large database of about 5700 anomalies registered by 220 satellites in different orbits over the period of 23 years (1971– 1994). Their findings revealed that very intense fluxes (>1000 particles cm<sup>−</sup><sup>2</sup> s<sup>−</sup><sup>1</sup> sr<sup>−</sup><sup>1</sup> (pfu) at energy >10 MeV) of solar protons are linked to anomalies registered by the satellites in high-altitude (>15,000 km) near-polar (inclination >55°) orbits and to a much smaller extent to anomalies in geostationary orbits. They also reported that elevated fluxes of energetic (>2 MeV) electrons >10 8 cm<sup>−</sup><sup>2</sup> d<sup>−</sup><sup>1</sup> sr<sup>−</sup><sup>1</sup> are observed by the Geostationary Operational Environmental Satellites (GOES) on days with satellite anomalies occurring at geostationary and low-altitude (<1500 km) nearpolar (>55°) orbits [3]. On the 22nd and 23rd of March 1991, an intense solar event occurred, which resulted to severe geomagnetic storms. This strong solar flare event with high energetic solar radiation caused disruption in high latitude point-to-point communication, and solar panel degradation on GOES-6 and -7 satellites, and was estimated to have decreased the expected lifetime of GOES-7 by 2 to 3 years. During the event, high energetic solar particles also increased the frequency of single event upsets (SEU) recorded by the spacecrafts; up to six geostationary satellites, including GOES-6 and -7, and the Tracking and Data Relay Satellite (TDRS)-1 had about 37 reported cases of SEU during the main phase of the event. SEU will be explained in detail in Section 3.2. Other impacts associated with this solar activity include the loss of automatic altitude control of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite, increased satellite drag due to the heated atmosphere, which necessitated a massive update of the North American Air Defense Command (NORAD) catalogue of orbiting objects, and the complete failure of the geosynchronous orbiting Maritime European Communication Satellite (MARECS)-1 as a result of critical damage to its solar panels [4, 5].

On September 2009, South Africa's SumbandilaSat (in low Earth orbit [LEO]) was reported to have experienced a power distribution failure due to radiation shortly after its launch, which rendered the Z- and Y-axis wheel permanently inoperable. However, the satellite continued to work as a technology demonstrator until 25 August 2011 when it failed completely. Its failure was again attributed to solar storm event, which caused the satellite's onboard computer to stop responding to commands from the ground station [6]. On 5 April 2010, Galaxy 15 spacecraft (at geosynchronous altitudes) was reported to have experienced an anomaly that caused it to stop responding to any ground command [7]. The failure was attributed to an onboard electrostatic discharge (ESD), which led to a lockup of the fieldprogrammable gate array within the spacecraft baseband communications unit. The interaction of the spacecraft with substorm-injected energetic particles caused the ESD after the spacecraft experienced surface and deep dielectric charging. A concise documentation of many other cases of satellite anomalies and losses that have been attributed to space weather can be found in several literatures (e.g., p. 33 of Refs. [8] and [9].

**75**

**Figure 1.**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

The Sun's activity varies with time and position on the Sun, and characterized by 11-year cycle, which can be divided into solar minimum and solar maximum phases. The sunspots (and other solar indices such as solar radio flux) are viewed as main indicators of solar activity cycle. They are transient phenomenon seen as dark patches against photospheric bright background on the Sun. Observations made over the past two centuries have shown that the number of sunspots vary periodically, moving from minimum to maximum count approximately every 11 years. **Figure 1** show a historic sunspot number. The latest solar cycle (cycle 24) peaked around year 2014. Currently, solar activity is on the decline and has been predicted to reach its minimum in late 2019 or 2020, while the solar maximum is expected to

Solar energetic events such as high-speed solar wind streams (HSS), solar flares and CMEs that give rise to solar particle events and geomagnetic storms affecting the space environment are more frequent during solar maximum. Therefore, their impact on the atmosphere and air-based technology are expected to be higher during this phase of the solar cycle than the declining or minimum phase. Solar events and associated phenomena mainly contribute to trapped and transient energetic particles in near space that constitute the space radiation environment, in addition to galactic cosmic ray from outer space. The summary of types of space radiation, their origin or sources, and where they are important is shown in **Figure 2**.

When charged particles from the solar wind encounters and interacts with the Earth's magnetic field, it compresses it sun-ward, forming the magnetosphere (see, **Figure 3**). This scenario creates a supersonic shock wave known as the Bow Shock. The solar wind drags out the night-side of the inner magnetosphere. This extension is known as the magnetotail. Although the magnetosphere is constantly being bombarded by charged particles, they are being deflected and cannot easily penetrate the region; however, some particles gain entrance through the polar region and become trapped in the Earth's magnetic field. The trapped particles are contained in one of two doughnut-shaped magnetic rings surrounding the Earth called the Van Allen radiation belts, **Figure 3**. The inner belt contains a fairly stable population of protons with energies exceeding 10 MeV. The outer belt contains mainly electrons

*Historic sunspot number (source: SILSO graphics (http://sidc.be/silso) Royal Observatory of Belgium).*

**2. Solar activity and the space radiation environment**

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

occur between 2023 and 2026 [10].

**2.1 The trapped particles**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

#### **2. Solar activity and the space radiation environment**

The Sun's activity varies with time and position on the Sun, and characterized by 11-year cycle, which can be divided into solar minimum and solar maximum phases. The sunspots (and other solar indices such as solar radio flux) are viewed as main indicators of solar activity cycle. They are transient phenomenon seen as dark patches against photospheric bright background on the Sun. Observations made over the past two centuries have shown that the number of sunspots vary periodically, moving from minimum to maximum count approximately every 11 years. **Figure 1** show a historic sunspot number. The latest solar cycle (cycle 24) peaked around year 2014. Currently, solar activity is on the decline and has been predicted to reach its minimum in late 2019 or 2020, while the solar maximum is expected to occur between 2023 and 2026 [10].

Solar energetic events such as high-speed solar wind streams (HSS), solar flares and CMEs that give rise to solar particle events and geomagnetic storms affecting the space environment are more frequent during solar maximum. Therefore, their impact on the atmosphere and air-based technology are expected to be higher during this phase of the solar cycle than the declining or minimum phase. Solar events and associated phenomena mainly contribute to trapped and transient energetic particles in near space that constitute the space radiation environment, in addition to galactic cosmic ray from outer space. The summary of types of space radiation, their origin or sources, and where they are important is shown in **Figure 2**.

#### **2.1 The trapped particles**

*Satellites Missions and Technologies for Geosciences*

both trapped and transient energetic particles since they are basically designed to operate in the space plasma environment. The particles can bombard and interact with satellites' surfaces, and sometimes posses enough energy to penetrate their exposed surfaces with possible access to their electrical, electronic and electrochemical components (EEECs). This scenario can induce sporadic and unexplainable errors in sensitive parts of spacecrafts, degrade the critical properties of their structural materials, jeopardize the flight worthiness of spacecrafts, constitute transient and terminal health hazard to both onboard passengers and astronauts, and even

lead to total failure that can end the mission of affected spacecrafts [1, 2].

elevated fluxes of energetic (>2 MeV) electrons >10 8 cm<sup>−</sup><sup>2</sup>

(MARECS)-1 as a result of critical damage to its solar panels [4, 5].

On September 2009, South Africa's SumbandilaSat (in low Earth orbit [LEO]) was reported to have experienced a power distribution failure due to radiation shortly after its launch, which rendered the Z- and Y-axis wheel permanently inoperable. However, the satellite continued to work as a technology demonstrator until 25 August 2011 when it failed completely. Its failure was again attributed to solar storm event, which caused the satellite's onboard computer to stop responding to commands from the ground station [6]. On 5 April 2010, Galaxy 15 spacecraft (at geosynchronous altitudes) was reported to have experienced an anomaly that caused it to stop responding to any ground command [7]. The failure was attributed to an onboard electrostatic discharge (ESD), which led to a lockup of the fieldprogrammable gate array within the spacecraft baseband communications unit. The interaction of the spacecraft with substorm-injected energetic particles caused the ESD after the spacecraft experienced surface and deep dielectric charging. A concise documentation of many other cases of satellite anomalies and losses that have been attributed to space weather can be found in several literatures (e.g., p. 33

There are documented cases or evidence of satellites anomaly associated with space weather (or space radiation environment). In their study, Iucci et al. [3] verified and quantified the linkage between a large fraction of spacecraft anomalies and space weather perturbations. They compiled a large database of about 5700 anomalies registered by 220 satellites in different orbits over the period of 23 years (1971– 1994). Their findings revealed that very intense fluxes (>1000 particles cm<sup>−</sup><sup>2</sup>

(pfu) at energy >10 MeV) of solar protons are linked to anomalies registered by the satellites in high-altitude (>15,000 km) near-polar (inclination >55°) orbits and to a much smaller extent to anomalies in geostationary orbits. They also reported that

by the Geostationary Operational Environmental Satellites (GOES) on days with satellite anomalies occurring at geostationary and low-altitude (<1500 km) nearpolar (>55°) orbits [3]. On the 22nd and 23rd of March 1991, an intense solar event occurred, which resulted to severe geomagnetic storms. This strong solar flare event with high energetic solar radiation caused disruption in high latitude point-to-point communication, and solar panel degradation on GOES-6 and -7 satellites, and was estimated to have decreased the expected lifetime of GOES-7 by 2 to 3 years. During the event, high energetic solar particles also increased the frequency of single event upsets (SEU) recorded by the spacecrafts; up to six geostationary satellites, including GOES-6 and -7, and the Tracking and Data Relay Satellite (TDRS)-1 had about 37 reported cases of SEU during the main phase of the event. SEU will be explained in detail in Section 3.2. Other impacts associated with this solar activity include the loss of automatic altitude control of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite, increased satellite drag due to the heated atmosphere, which necessitated a massive update of the North American Air Defense Command (NORAD) catalogue of orbiting objects, and the complete failure of the geosynchronous orbiting Maritime European Communication Satellite

 s<sup>−</sup><sup>1</sup> sr<sup>−</sup><sup>1</sup>

are observed

 d<sup>−</sup><sup>1</sup> sr<sup>−</sup><sup>1</sup>

**74**

of Refs. [8] and [9].

When charged particles from the solar wind encounters and interacts with the Earth's magnetic field, it compresses it sun-ward, forming the magnetosphere (see, **Figure 3**). This scenario creates a supersonic shock wave known as the Bow Shock. The solar wind drags out the night-side of the inner magnetosphere. This extension is known as the magnetotail. Although the magnetosphere is constantly being bombarded by charged particles, they are being deflected and cannot easily penetrate the region; however, some particles gain entrance through the polar region and become trapped in the Earth's magnetic field. The trapped particles are contained in one of two doughnut-shaped magnetic rings surrounding the Earth called the Van Allen radiation belts, **Figure 3**. The inner belt contains a fairly stable population of protons with energies exceeding 10 MeV. The outer belt contains mainly electrons

**Figure 1.** *Historic sunspot number (source: SILSO graphics (http://sidc.be/silso) Royal Observatory of Belgium).*

#### **Figure 2.**

*Summary of types of space radiation, their origin or sources, and where they are important in the outer planets, planetary space and Earth, including the low Earth orbit (LEO), geostationary orbit (GEO), medium Earth orbit (MEO) and high Earth orbit (HEO) (source: Ref. [11]).*

#### **Figure 3.**

*(a) The Earth's magnetosphere showing the Van Allen radiation belt. (b) Outer and inner (proton) belt (source: Ref. [12]).*

with energies up to 10 MeV. The charged particles which compose the belts circulate along the Earth's magnetic lines of force. These lines of force are known to extend from the area above the equator to the North Pole, to the South Pole, and then circle back to the Equator. There is a part of the inner Van Allen belt (VAB) that dips down to about 200 km into the upper region of the atmosphere over the southern Atlantic Ocean off the coast of Brazil. This region is known as the South Atlantic Anomaly (SAA). The dip results from the fact that the magnetic axis of the Earth is tilted approximately 11° from the spin axis, and the center of the magnetic field is offset from the geographical center of the Earth by 280 miles. The largest fraction of the radiation exposure received during spaceflight missions has resulted from passage through the SAA. Low inclination flights typically traverse a portion of the SAA up to six or seven times a day (see **Figure 3**).

#### **2.2 The transient particles**

The transient particles or radiation environments consist of particles from solar events such as solar wind, solar flares, CMEs and galactic cosmic radiation in the interplanetary and near-Earth space regions. The solar wind consists of relatively low energy electrons and protons that can significantly affect externally mounted spacecraft components. Solar flares are also a major contributor to the overall

**77**

**3.1 Spacecraft charging (SC)**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

ionizing radiation level. A solar flare can emit and accelerate energetic particles or protons in the interplanetary space that can reach Earth within 30 minutes of the flare's peak. CMEs can propagate into the solar wind and drive shocks, which in turn accelerates solar energetic particles, and also deflect the galactic cosmic rays (GCRs) entering the heliosphere [13, 14]. CME can cause geomagnetic storms and other associated phenomena, leading to large-scale disturbances with adverse consequences in the geospace environment that can affect satellite systems.

Galactic cosmic radiations (GCR) are not directly connected to our Sun. They originate from outside the solar system. GCR consists of ionized atoms ranging from a single proton up to a uranium nucleus. The flux level of these particles is very low. Notwithstanding, they produce intense ionization as they pass through matter because they travel at a speed that is very close to that of light, and because some of them are composed of very heavy elements such as iron [15]. The energy of cosmic rays is usually measured in units of mega electron volt (MeV), or the giga electron volt (GeV). Most GCRs have energies between 100 MeV and 10 GeV. Cosmic rays include essentially all of the elements in the periodic table; about 85% protons, 14% alpha particles, and 1% heavy nuclei [16]. The Earth's magnetic field provides natural shielding from both cosmic and solar particles depending primarily on the inclination and secondarily on the altitude. As inclination reaches auroral to polar regions, a satellite is outside the protection of the geomagnetic field lines. At polar orbits intense fluxes of energetic electrons, known as precipitating electrons, propagate down along magnetic field lines (and create the aurora), and as altitude

increases, the exposure to these particles gradually increases [12].

environment are summarized below according to the particle source.

**operation in near-Earth space environment**

**3. Effects of space particles and radiation environment on satellites** 

When charged trapped or transient particles from solar events or cosmic sources bombards and interacts with the exposed surfaces of spacecraft, their effects can affect the system in a several ways. The effects from the natural space environment include spacecraft charging (SC), single event effects (SEEs), total ionizing dose (TID), and displacement damage (DD). However, the specific effect depends on the type of incident particle, its energy and probably the source. Trapped heavy ions do not have sufficient energy to generate the ionization required to cause SEEs, and they do not make a significant contribution to TID. Galactic cosmic rays and cosmic solar particles, which are heavily influenced by solar flares and trapped protons in the radiation belts, can cause SEEs, but electrons are not known to cause SEEs. Although their physical mechanisms are different, the ionizing radiation of the space environment causes both TID and SEEs. Charged particle effects in the space

Spacecraft charging (SC) is the build-up of charge on spacecraft surfaces or in the spacecraft interior; SC causes variations in the electrostatic potential of a spacecraft surface with respect to the surrounding plasma environment, and potential variations in different portions of the spacecraft [17]. The major natural space environments which contribute to SC include the thermal plasma environment, high energy electrons, solar radiation and magnetic fields. Although SC has many

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

**2.3 Galactic cosmic radiation**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

ionizing radiation level. A solar flare can emit and accelerate energetic particles or protons in the interplanetary space that can reach Earth within 30 minutes of the flare's peak. CMEs can propagate into the solar wind and drive shocks, which in turn accelerates solar energetic particles, and also deflect the galactic cosmic rays (GCRs) entering the heliosphere [13, 14]. CME can cause geomagnetic storms and other associated phenomena, leading to large-scale disturbances with adverse consequences in the geospace environment that can affect satellite systems.

#### **2.3 Galactic cosmic radiation**

*Satellites Missions and Technologies for Geosciences*

*orbit (MEO) and high Earth orbit (HEO) (source: Ref. [11]).*

**Figure 2.**

**Figure 3.**

*(source: Ref. [12]).*

with energies up to 10 MeV. The charged particles which compose the belts circulate along the Earth's magnetic lines of force. These lines of force are known to extend from the area above the equator to the North Pole, to the South Pole, and then circle back to the Equator. There is a part of the inner Van Allen belt (VAB) that dips down to about 200 km into the upper region of the atmosphere over the southern Atlantic Ocean off the coast of Brazil. This region is known as the South Atlantic Anomaly (SAA). The dip results from the fact that the magnetic axis of the Earth is tilted approximately 11° from the spin axis, and the center of the magnetic field is offset from the geographical center of the Earth by 280 miles. The largest fraction of the radiation exposure received during spaceflight missions has resulted from passage through the SAA. Low inclination flights typically traverse a portion of the SAA up

*(a) The Earth's magnetosphere showing the Van Allen radiation belt. (b) Outer and inner (proton) belt* 

*Summary of types of space radiation, their origin or sources, and where they are important in the outer planets, planetary space and Earth, including the low Earth orbit (LEO), geostationary orbit (GEO), medium Earth* 

The transient particles or radiation environments consist of particles from solar events such as solar wind, solar flares, CMEs and galactic cosmic radiation in the interplanetary and near-Earth space regions. The solar wind consists of relatively low energy electrons and protons that can significantly affect externally mounted spacecraft components. Solar flares are also a major contributor to the overall

**76**

to six or seven times a day (see **Figure 3**).

**2.2 The transient particles**

Galactic cosmic radiations (GCR) are not directly connected to our Sun. They originate from outside the solar system. GCR consists of ionized atoms ranging from a single proton up to a uranium nucleus. The flux level of these particles is very low. Notwithstanding, they produce intense ionization as they pass through matter because they travel at a speed that is very close to that of light, and because some of them are composed of very heavy elements such as iron [15]. The energy of cosmic rays is usually measured in units of mega electron volt (MeV), or the giga electron volt (GeV). Most GCRs have energies between 100 MeV and 10 GeV. Cosmic rays include essentially all of the elements in the periodic table; about 85% protons, 14% alpha particles, and 1% heavy nuclei [16]. The Earth's magnetic field provides natural shielding from both cosmic and solar particles depending primarily on the inclination and secondarily on the altitude. As inclination reaches auroral to polar regions, a satellite is outside the protection of the geomagnetic field lines. At polar orbits intense fluxes of energetic electrons, known as precipitating electrons, propagate down along magnetic field lines (and create the aurora), and as altitude increases, the exposure to these particles gradually increases [12].

#### **3. Effects of space particles and radiation environment on satellites operation in near-Earth space environment**

When charged trapped or transient particles from solar events or cosmic sources bombards and interacts with the exposed surfaces of spacecraft, their effects can affect the system in a several ways. The effects from the natural space environment include spacecraft charging (SC), single event effects (SEEs), total ionizing dose (TID), and displacement damage (DD). However, the specific effect depends on the type of incident particle, its energy and probably the source. Trapped heavy ions do not have sufficient energy to generate the ionization required to cause SEEs, and they do not make a significant contribution to TID. Galactic cosmic rays and cosmic solar particles, which are heavily influenced by solar flares and trapped protons in the radiation belts, can cause SEEs, but electrons are not known to cause SEEs. Although their physical mechanisms are different, the ionizing radiation of the space environment causes both TID and SEEs. Charged particle effects in the space environment are summarized below according to the particle source.

#### **3.1 Spacecraft charging (SC)**

Spacecraft charging (SC) is the build-up of charge on spacecraft surfaces or in the spacecraft interior; SC causes variations in the electrostatic potential of a spacecraft surface with respect to the surrounding plasma environment, and potential variations in different portions of the spacecraft [17]. The major natural space environments which contribute to SC include the thermal plasma environment, high energy electrons, solar radiation and magnetic fields. Although SC has many

effects, electrostatic discharges appear to be the most dangerous of all. Electrostatic discharges can cause structural damage, degradation of spacecraft components and operational anomalies due to damages to electronics. SC can be categorised into two: Surface charging which include differential charging, and internal dielectric charging. Surface charging is caused by low energy plasma (<100 keV) and photoelectric currents. Surface charging can either be absolute or differential. Absolute charging occurs when the satellite potential relative to the ambient plasma is charged uniformly, while differential charging occurs when parts of the spacecraft are charged to different potential relative to one another. Differential charging can also be caused by satellite self-shadowing. The charge control mechanism, and differential charging in spacecrafts are depicted in **Figure 4**. Differential charging of spacecraft surfaces is more detrimental than the absolute charging (relative to ambient plasma). The former can have a discharge effects that can disrupt satellite operations such as physical materials damage and electromagnetic interference (EMI) generation, and resultant transient pulses. Discharge consequences also include noise in data and wiring, sputtering and attraction of chemically active species [18]. Differential charging has been reported after geomagnetic sub-storms, which result in the injection of keV electrons into the magnetosphere.

Internal charging is caused by high-energy electrons (>100 keV), which penetrate into the spacecraft equipment where they deposit charge inside insulating materials [8]. Internal discharge is more damaging since it occurs within dielectric materials and well-insulated conductors, which are in close proximity to sensitive electronic circuitry [19]. Based on data from the Combined Release and Radiation Effects Satellite (CRRES) obtained at GEO, most environmentally induced spacecraft anomalies result from deep dielectric charging and the resulting discharge pulses and not from surface insulator charging or single-event upsets [20].

#### **3.2 Single event effects (SEEs)**

Single event effects (SEEs) are individual events which occur when a single incident ionizing particle deposits enough energy to cause an effect in a device. SEEs are generally caused by two space radiation sources: high energy protons, and cosmic rays. Single event phenomenon can be classified into four: (i) single event upset (SEU), (ii) single event latch-up (SEL), (iii) single event burnout (SEB) and (iv) single event gate rupture (SEGR). SEU is a change of state caused by ions or electromagnetic radiation striking a sensitive node in a micro-electronic device, such as in a microprocessor, semiconductor memory, or power transistors. The state change is a result of the free charge created by ionization in or close to an important node of a logic element (e.g., memory bit). The error in device output or operation

#### **Figure 4.**

*(a) Satellite's charge control mechanism, and (b) differential charging in satellites due to self-shadowing (source: Ref. [12]).*

**79**

**Figure 5.**

BJTs, and CMOS.

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

SEFI halts normal operations, and requires a power reset to recover [1].

caused as a result of the strike is called a soft error. The mechanisms for heavy ion and proton SEU in devices (e.g., dynamic random access memories (DRAM)), and galactic cosmic ray energy deposition in devices are depicted in **Figure 5**. SEU can cause a reset or re-writing in normal device such as in analogue, digital, or optical components, and may also have effects in surrounding interface circuitry. A severe SEU is the single-event functional interrupt (SEFI) in which an SEU in the device's control circuitry places the device into a test mode, halt, or undefined state. The

SEL is used in integrated circuits (ICs) to describe a particular type of short circuit which can occur in an improperly designed circuit. It is the generation of a low-impedance path between the power supply rails of a MOSFET circuit that can trigger a parasitic structure which disrupts proper functioning of the part and possibly even leading to its destruction due to over-current. SELs are hard errors, and can cause permanent damage. It can results in a high operating current, above device specifications, drag down the bus voltage, or damage the power supply. Latch-up can be caused by protons in very sensitive devices [22]. An SEL is corrected or cleared by a power off–on reset or power strobing of the device. SEL is strongly temperature dependent. If power is not removed quickly, catastrophic failure may occur due to excessive heating or metallization or bond wire failure [23]. SEB is a condition caused by high current state in a power transistor. It is a highly localized phenomenon, and includes burnout of the drain-source in power MOSFETs and BJTs, gate rupture, frozen bits, and noise in charged-coupled devices (CCDs). SEGR is the formation of a conducting path or localized dielectric breakdown in the gate oxide resulting in a destructive burnout. It occurs at MOSFETs,

*(a) Mechanisms for heavy ion and proton SEU, (b) schematic showing how GCR deposit energy in an electronic device [12], and (c) upset mechanism for dynamic random-access memories (DRAMs) (from Ref. [21]).*

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

#### *The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

caused as a result of the strike is called a soft error. The mechanisms for heavy ion and proton SEU in devices (e.g., dynamic random access memories (DRAM)), and galactic cosmic ray energy deposition in devices are depicted in **Figure 5**. SEU can cause a reset or re-writing in normal device such as in analogue, digital, or optical components, and may also have effects in surrounding interface circuitry. A severe SEU is the single-event functional interrupt (SEFI) in which an SEU in the device's control circuitry places the device into a test mode, halt, or undefined state. The SEFI halts normal operations, and requires a power reset to recover [1].

SEL is used in integrated circuits (ICs) to describe a particular type of short circuit which can occur in an improperly designed circuit. It is the generation of a low-impedance path between the power supply rails of a MOSFET circuit that can trigger a parasitic structure which disrupts proper functioning of the part and possibly even leading to its destruction due to over-current. SELs are hard errors, and can cause permanent damage. It can results in a high operating current, above device specifications, drag down the bus voltage, or damage the power supply. Latch-up can be caused by protons in very sensitive devices [22]. An SEL is corrected or cleared by a power off–on reset or power strobing of the device. SEL is strongly temperature dependent. If power is not removed quickly, catastrophic failure may occur due to excessive heating or metallization or bond wire failure [23].

SEB is a condition caused by high current state in a power transistor. It is a highly localized phenomenon, and includes burnout of the drain-source in power MOSFETs and BJTs, gate rupture, frozen bits, and noise in charged-coupled devices (CCDs). SEGR is the formation of a conducting path or localized dielectric breakdown in the gate oxide resulting in a destructive burnout. It occurs at MOSFETs, BJTs, and CMOS.

#### **Figure 5.**

*(a) Mechanisms for heavy ion and proton SEU, (b) schematic showing how GCR deposit energy in an electronic device [12], and (c) upset mechanism for dynamic random-access memories (DRAMs) (from Ref. [21]).*

*Satellites Missions and Technologies for Geosciences*

**3.2 Single event effects (SEEs)**

effects, electrostatic discharges appear to be the most dangerous of all. Electrostatic discharges can cause structural damage, degradation of spacecraft components and operational anomalies due to damages to electronics. SC can be categorised into two: Surface charging which include differential charging, and internal dielectric charging. Surface charging is caused by low energy plasma (<100 keV) and photoelectric currents. Surface charging can either be absolute or differential. Absolute charging occurs when the satellite potential relative to the ambient plasma is charged uniformly, while differential charging occurs when parts of the spacecraft are charged to different potential relative to one another. Differential charging can also be caused by satellite self-shadowing. The charge control mechanism, and differential charging in spacecrafts are depicted in **Figure 4**. Differential charging of spacecraft surfaces is more detrimental than the absolute charging (relative to ambient plasma). The former can have a discharge effects that can disrupt satellite operations such as physical materials damage and electromagnetic interference (EMI) generation, and resultant transient pulses. Discharge consequences also include noise in data and wiring, sputtering and attraction of chemically active species [18]. Differential charging has been reported after geomagnetic sub-storms,

which result in the injection of keV electrons into the magnetosphere.

Internal charging is caused by high-energy electrons (>100 keV), which penetrate into the spacecraft equipment where they deposit charge inside insulating materials [8]. Internal discharge is more damaging since it occurs within dielectric materials and well-insulated conductors, which are in close proximity to sensitive electronic circuitry [19]. Based on data from the Combined Release and Radiation Effects Satellite (CRRES) obtained at GEO, most environmentally induced spacecraft anomalies result from deep dielectric charging and the resulting discharge pulses and not from surface insulator charging or single-event upsets [20].

Single event effects (SEEs) are individual events which occur when a single incident ionizing particle deposits enough energy to cause an effect in a device. SEEs are generally caused by two space radiation sources: high energy protons, and cosmic rays. Single event phenomenon can be classified into four: (i) single event upset (SEU), (ii) single event latch-up (SEL), (iii) single event burnout (SEB) and (iv) single event gate rupture (SEGR). SEU is a change of state caused by ions or electromagnetic radiation striking a sensitive node in a micro-electronic device, such as in a microprocessor, semiconductor memory, or power transistors. The state change is a result of the free charge created by ionization in or close to an important node of a logic element (e.g., memory bit). The error in device output or operation

*(a) Satellite's charge control mechanism, and (b) differential charging in satellites due to self-shadowing* 

**78**

**Figure 4.**

*(source: Ref. [12]).*

Solar flare particle events pose the most extreme SEU producing environment, especially for spacecraft in interplanetary space [24]. Experiments aboard CRRES showed a significant increase during a solar flare [25]. Based on CRRES's data, most SEUs come from high energy protons through nuclear interactions and not through direct deposition from either protons or cosmic rays [20]. For LEO satellites, trapped protons, especially in the SAA, are the greatest SEE threat.

#### **3.3 Total ionizing dose (TID)**

Total ionizing dose (TID) refers to the amount of energy that ionization processes create and deposit in materials such as semiconductor or insulator when energized particles pass through it. TID can result in device failure or biological damage to astronauts. Radiation-induced trapped charges can build up in the gate oxide of a MOSFET and cause a shift in the threshold voltage. Such device cannot be turned off even at zero volts applied, if the shift is large enough. Under this condition the device is said to have failed by going into depletion mode [26]. TID is mostly due to electrons and protons, mainly from solar energetic particle event and passage through the SAA. In low Earth orbit, the main dose source is from electrons and inner belt protons, while the primary source is outer belt electron and solar protons in geostationary orbit. The first recorded satellite failure resulting from total dose was the Telstar. The satellite was launched a day after the Starfish nuclear test on 9 July 1962. The nuclear weapon of about 1.4 Megaton was detonated at an altitude of about 400 km above Johnston Island in the Pacific Ocean. The explosion produced beta particles (electrons) that were injected into the Earth's magnetic field, forming an artificial radiation belt. This artificial electron belt lasted until the early 1970s. Consequently, Telstar experienced a total dose 100 times that expected before its total failure. Up to seven satellites were destroyed by the Starfish nuclear test within 7 months mainly from solar cell damage [12].

#### **3.4 Displacement damage (DD)**

When energetic particles are incident on a solid material, they lose their energy to ionizing and non-ionizing processes as they travel through the material. The consequence of the energy loss is in the production of electron–hole pairs and atoms displacement or displacement damage. Vacancies (i.e., absence of an atom from its normal lattice position) and interstitials (i.e., movement of displaced atom into a non-lattice position) are the primary lattice defects that are initially created. The combination of a vacancy and an adjacent interstitial is known as a Frenkel or close pair. Two adjacent vacancies can form a defect known as divacancy. Also, larger local groupings of vacancies may occur in irradiated silicon. A defect resulting from vacancy and interstitials being adjacent to impurity atoms is known as defect-impurity complexes. Once formed by incident radiation, the defects will reorder to form more stable configuration. The extent to which defects alter the properties of bulk semiconductor material and devices depends on nature of the particular defects and the time following the creation of defect at a given temperature.

The effectiveness of radiation-induced displacement damage depends on factors such as bombardment condition, particle type and energy, irradiation and measurement temperature, time after irradiation, thermal history after irradiation, injection level, material type, impurity type and concentration [27]. Displacement damage causes degradation of materials and device properties. **Figure 6** depicts the collision between an incoming particle and a lattice atom, causing the displacement

**81**

given by:

*S* = −

constant (= 8.99 × 109

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

of the atom from its original lattice position. Displacement damage can also degrade minority carrier lifetime, and a typical effect would be degradation of gain and

The review presented here include portion of the work [1], part of which was published in [2]. We analyzed particles, electrons and protons flux of various energies from NOAA database for 3 months (April–June 2010). The mass stopping power, range and possible deposited dose of protons were calculated, and applied to the scenario of possible interaction of the particles with satellite surface and its

Stopping power is the average energy loss of a particle per unit length (measured in MeV/cm) when passing through the material. Charged particles are known to ionize the atom or molecule which they encounter when passing through matter, and they lose energy in the process. The stopping power depends on the type and energy of the particle and on the properties of the material it passes. Although numerical values and units are identical for both quantities, the Stopping power refers to the property of the material while energy loss per unit path length describes what happens to the particle. The density of ionization along the particles path is proportional to the stopping power of the material because the production of an ion pairs requires a fixed amount of energy [28]. The Bethe-Bloch formula for stopping power derived from relativistic quantum mechanics is

**4. The stopping power, range of particles and deposited dose in** 

*Displacement of atom from its original lattice position by incoming particle through collision [12].*

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

leakage current in bipolar transistors [12].

electrical, electronic and electrochemical components.

\_ *dE dx* = 4*<sup>π</sup> <sup>z</sup>*<sup>2</sup>

N m2

 C<sup>−</sup><sup>2</sup> ).

 *k*0 2 *e* \_

*m v*<sup>2</sup>

4

[ln

2*m v* \_ 2 *<sup>I</sup>* <sup>−</sup> *ln* (<sup>1</sup> <sup>−</sup>

where *z* is the atomic number of the heavy particle, *e* is magnitude of the electron charge, *m* is the electron rest mass, *c* is the speed of light, *I* is the mean excitation energy of the medium, *v* is the velocity of the particle and *k*0 is the Boltzmann

*v*2 \_ *c*2 ) − *v*2 \_ *c*2 ]

**spacecraft materials**

**Figure 6.**

**4.1 Stopping power**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

**Figure 6.**

*Satellites Missions and Technologies for Geosciences*

**3.3 Total ionizing dose (TID)**

Solar flare particle events pose the most extreme SEU producing environment, especially for spacecraft in interplanetary space [24]. Experiments aboard CRRES showed a significant increase during a solar flare [25]. Based on CRRES's data, most SEUs come from high energy protons through nuclear interactions and not through direct deposition from either protons or cosmic rays [20]. For LEO satellites,

Total ionizing dose (TID) refers to the amount of energy that ionization processes create and deposit in materials such as semiconductor or insulator when energized particles pass through it. TID can result in device failure or biological damage to astronauts. Radiation-induced trapped charges can build up in the gate oxide of a MOSFET and cause a shift in the threshold voltage. Such device cannot be turned off even at zero volts applied, if the shift is large enough. Under this condition the device is said to have failed by going into depletion mode [26]. TID is mostly due to electrons and protons, mainly from solar energetic particle event and passage through the SAA. In low Earth orbit, the main dose source is from electrons and inner belt protons, while the primary source is outer belt electron and solar protons in geostationary orbit. The first recorded satellite failure resulting from total dose was the Telstar. The satellite was launched a day after the Starfish nuclear test on 9 July 1962. The nuclear weapon of about 1.4 Megaton was detonated at an altitude of about 400 km above Johnston Island in the Pacific Ocean. The explosion produced beta particles (electrons) that were injected into the Earth's magnetic field, forming an artificial radiation belt. This artificial electron belt lasted until the early 1970s. Consequently, Telstar experienced a total dose 100 times that expected before its total failure. Up to seven satellites were destroyed by the Starfish nuclear

When energetic particles are incident on a solid material, they lose their energy to ionizing and non-ionizing processes as they travel through the material. The consequence of the energy loss is in the production of electron–hole pairs and atoms displacement or displacement damage. Vacancies (i.e., absence of an atom from its normal lattice position) and interstitials (i.e., movement of displaced atom into a non-lattice position) are the primary lattice defects that are initially created. The combination of a vacancy and an adjacent interstitial is known as a Frenkel or close pair. Two adjacent vacancies can form a defect known as divacancy. Also, larger local groupings of vacancies may occur in irradiated silicon. A defect resulting from vacancy and interstitials being adjacent to impurity atoms is known as defect-impurity complexes. Once formed by incident radiation, the defects will reorder to form more stable configuration. The extent to which defects alter the properties of bulk semiconductor material and devices depends on nature of the particular defects and the time following the creation of defect at a given

The effectiveness of radiation-induced displacement damage depends on factors such as bombardment condition, particle type and energy, irradiation and measurement temperature, time after irradiation, thermal history after irradiation, injection level, material type, impurity type and concentration [27]. Displacement damage causes degradation of materials and device properties. **Figure 6** depicts the collision between an incoming particle and a lattice atom, causing the displacement

trapped protons, especially in the SAA, are the greatest SEE threat.

test within 7 months mainly from solar cell damage [12].

**3.4 Displacement damage (DD)**

**80**

temperature.

*Displacement of atom from its original lattice position by incoming particle through collision [12].*

of the atom from its original lattice position. Displacement damage can also degrade minority carrier lifetime, and a typical effect would be degradation of gain and leakage current in bipolar transistors [12].

#### **4. The stopping power, range of particles and deposited dose in spacecraft materials**

The review presented here include portion of the work [1], part of which was published in [2]. We analyzed particles, electrons and protons flux of various energies from NOAA database for 3 months (April–June 2010). The mass stopping power, range and possible deposited dose of protons were calculated, and applied to the scenario of possible interaction of the particles with satellite surface and its electrical, electronic and electrochemical components.

#### **4.1 Stopping power**

Stopping power is the average energy loss of a particle per unit length (measured in MeV/cm) when passing through the material. Charged particles are known to ionize the atom or molecule which they encounter when passing through matter, and they lose energy in the process. The stopping power depends on the type and energy of the particle and on the properties of the material it passes. Although numerical values and units are identical for both quantities, the Stopping power refers to the property of the material while energy loss per unit path length describes what happens to the particle. The density of ionization along the particles path is proportional to the stopping power of the material because the production of an ion pairs requires a fixed amount of energy [28]. The Bethe-Bloch formula for stopping power derived from relativistic quantum mechanics is given by: *e* \_

en by: 
$$S = -\frac{dE}{dx} = \frac{4\pi x^2 k\_0^2 e^4}{m\nu^2} \left[ \ln \frac{2m\nu^2}{I} - \ln \left( \mathbf{1} - \frac{\nu^2}{c^2} \right) - \frac{\nu^2}{c^2} \right]$$

where *z* is the atomic number of the heavy particle, *e* is magnitude of the electron charge, *m* is the electron rest mass, *c* is the speed of light, *I* is the mean excitation energy of the medium, *v* is the velocity of the particle and *k*0 is the Boltzmann constant (= 8.99 × 109 N m2 C<sup>−</sup><sup>2</sup> ).

The mass stopping power of the material is obtained by dividing the stopping power by the density (*ρ*) of the material. It is a useful quantity because it expresses the rate of energy loss of the charged particle per g/cm2 of the medium traversed [28].

$$-\frac{1}{\tau}$$

$$S = -\frac{dE}{\rho dx}$$

#### **4.2 The range of particle**

The range *R* of a particle (e.g., proton) of initial kinetic energy *Ek* and mass *m* is the mean distance it travels before coming to a stop. *R* depends on the particle type, initial energy and the material through which it traverses. A theoretical approach to the determination of charged particle range utilizes stopping power expression. The range of a proton computed by numerical integration of the stopping power using the continuous slowing down approximation (CSDA) is given by:

$$\begin{aligned} \text{If } \mathbf{u} \text{ and } \mathbf{v} \text{ are } \dots \text{ then } \\ \mathbf{R} = \int\_{E\_{\text{min}}}^{E\_{\text{max}}} \left( -\frac{dE}{\rho dx} \right)^{-1} dE + R(E\_{\text{min}}) \end{aligned}$$

where *R* (*Emin*) is the measured range at minimum energy *Emin* which is added to the integral equation and treated as a constant for a particle and material. For the calculations of ranges for proton *Emin* is taken to be as 1 MeV as much data is available at 1 Mev. *R* (*Emax*) is the measured range at maximum energy *Emax.*

In previous work we used the empirical relations suggested by [28] to calculate the mass stopping power of particles in spacecraft materials [1, 2]. However, we anticipate limitations in the equations because they were originally formulated for low energy particles. Values obtained using Bethe's equations are higher and assumed more accurate at higher particle energies.

#### **4.3 Dose deposition and absorption**

The total ionizing dose (TID), explained in Section 3.3, can be measured in terms of the absorbed dose; which is a measure of the energy absorbed by matter. Absorbed dose is quantified using either a unit called the rad (radiation absorbed dose) or the SI unit which is the gray (Gy). 1 Gy = 100 rads = 1 J/kg. The total accumulated dose on a satellite depends on orbit altitude, orientation, and time spent in orbit. To compute TID we need to know the integrated particle energy spectrum, ø(*E*) or the fluence as a function of particle energy. The dose is a function of the particle flux. It becomes important as the spacecraft spends more time in the space radiation environment. The stopping power is used to determine dose from charged particle by the following relationship:

 $\omega\_1$  הוא  $\omega\_2$  הוא הוא הוא הוא  $\omega\_3$  
$$D = \mathcal{Q} \frac{dE}{\rho dx}$$

where ∅ is the particle fluence (i.e., the number of particles striking the material over a specified time interval).

Satellite and space probes typically encounter TID between 10 krad (100 Gy) and 100 krad(Si) (1000 Gy(Si)). The time taken, *t* (in years) for a satellite's component to fail due to total ionizing dose can be obtained by dividing the maximum absorbed dose or TID threshold by the total absorbed dose per year, given as: *<sup>t</sup>* (*in yrs*) = \_

$$t \text{ (} in \text{ } jrrs\text{) } = \frac{TID\_{\text{rhredolold}}}{Dose/yr}$$

**83**

**Figure 7.**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

model satellite's component within 3 years and 100 krad within 29 years.

The electrons impinging on spacecraft surface in the space environment are faster than their ion counterpart because of their very small mass (when compared to that of ions). As a result the ambient electron flux is usually more than the ambient ion flux, leading to high level negative charging of the spacecraft. The regions of concern (in space) for internal charging of spacecrafts is illustrated in [29] and shown in **Figure 7**. Spacecraft charging can be mitigated by the methods of electron emission and ion reception [30]. Electron emission is the method in which a device pulls (or draws) electrons from the spacecraft ground and ejects them into space, while the ion reception is the method in which positive ions arrive at a spacecraft that is negatively charged to neutralize the negative charges. The former method is effective for reducing the negative charge of the spacecraft ground but not effective for dielectric surfaces. As a demerit, the process can lead to differential charging between the dielectric and the conducting ground. The later method is effective for mitigating negatively charged surface (whether dielectric or conductor), and reducing differential charging. However, it has the disadvantage of electroplating the entire spacecraft with extended use. Because each method has advantage (or disadvantage) over the other, the use of a combination of both types has been recommended. Other mitigation methods include plasma emission, partially conducting paint, polar molecule emission, mirror reflection and violet

**5. Mitigating the impact of space radiation environment**

*Regions of concern for internal charging of spacecrafts in space (source: Ref. [29]).*

made of aluminum alloy and 20 mm thickness (without impact mitigation such as protective coating on the satellite), in which the electrical, electronic and electrochemical components (mainly of silicon (Si) and germanium (Ge) materials) are housed [1, 2]. Our calculations were based on particles with E ≥ 78 MeV. When particles of this energy range bombard and penetrate the satellite, parts of their energies are lost due to the stopping power of the alloy but the reminder constitute significant dose to the components. With continuous exposure, the dose continues to build over time until the threshold is exceeded leading to completed failure of the affected satellite. Our calculations showed that a dose of 10 krad can build up on the

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

**5.1 Spacecraft charging**

irradiation [31].

We performed theoretical calculations to predict the mean time to failure of a model satellite due to TID. The assumption is that the model satellite's body is

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

made of aluminum alloy and 20 mm thickness (without impact mitigation such as protective coating on the satellite), in which the electrical, electronic and electrochemical components (mainly of silicon (Si) and germanium (Ge) materials) are housed [1, 2]. Our calculations were based on particles with E ≥ 78 MeV. When particles of this energy range bombard and penetrate the satellite, parts of their energies are lost due to the stopping power of the alloy but the reminder constitute significant dose to the components. With continuous exposure, the dose continues to build over time until the threshold is exceeded leading to completed failure of the affected satellite. Our calculations showed that a dose of 10 krad can build up on the model satellite's component within 3 years and 100 krad within 29 years.

#### **5. Mitigating the impact of space radiation environment**

#### **5.1 Spacecraft charging**

*Satellites Missions and Technologies for Geosciences*

**4.2 The range of particle**

*R* = ∫

**4.3 Dose deposition and absorption**

particle by the following relationship:

over a specified time interval).

*D* = ∅

*<sup>t</sup>* (*in yrs*) = \_

the rate of energy loss of the charged particle per g/cm2

the continuous slowing down approximation (CSDA) is given by:

*Emin Emax* (− \_ *dE dx*) −1

assumed more accurate at higher particle energies.

able at 1 Mev. *R* (*Emax*) is the measured range at maximum energy *Emax.*

The mass stopping power of the material is obtained by dividing the stopping power by the density (*ρ*) of the material. It is a useful quantity because it expresses

> *S* = − \_ *dE dx*

The range *R* of a particle (e.g., proton) of initial kinetic energy *Ek* and mass *m* is the mean distance it travels before coming to a stop. *R* depends on the particle type, initial energy and the material through which it traverses. A theoretical approach to the determination of charged particle range utilizes stopping power expression. The range of a proton computed by numerical integration of the stopping power using

where *R* (*Emin*) is the measured range at minimum energy *Emin* which is added to the integral equation and treated as a constant for a particle and material. For the calculations of ranges for proton *Emin* is taken to be as 1 MeV as much data is avail-

In previous work we used the empirical relations suggested by [28] to calculate the mass stopping power of particles in spacecraft materials [1, 2]. However, we anticipate limitations in the equations because they were originally formulated for low energy particles. Values obtained using Bethe's equations are higher and

The total ionizing dose (TID), explained in Section 3.3, can be measured in terms of the absorbed dose; which is a measure of the energy absorbed by matter. Absorbed dose is quantified using either a unit called the rad (radiation absorbed dose) or the SI unit which is the gray (Gy). 1 Gy = 100 rads = 1 J/kg. The total accumulated dose on a satellite depends on orbit altitude, orientation, and time spent in orbit. To compute TID we need to know the integrated particle energy spectrum, ø(*E*) or the fluence as a function of particle energy. The dose is a function of the particle flux. It becomes important as the spacecraft spends more time in the space radiation environment. The stopping power is used to determine dose from charged

\_ *dE dx* where ∅ is the particle fluence (i.e., the number of particles striking the material

> *TIDthreshold Dose*/*yr*

Satellite and space probes typically encounter TID between 10 krad (100 Gy) and 100 krad(Si) (1000 Gy(Si)). The time taken, *t* (in years) for a satellite's component to fail due to total ionizing dose can be obtained by dividing the maximum absorbed dose or TID threshold by the total absorbed dose per year, given as:

We performed theoretical calculations to predict the mean time to failure of a model satellite due to TID. The assumption is that the model satellite's body is

*dE* + *R*(*Emin*)

of the medium traversed [28].

**82**

The electrons impinging on spacecraft surface in the space environment are faster than their ion counterpart because of their very small mass (when compared to that of ions). As a result the ambient electron flux is usually more than the ambient ion flux, leading to high level negative charging of the spacecraft. The regions of concern (in space) for internal charging of spacecrafts is illustrated in [29] and shown in **Figure 7**. Spacecraft charging can be mitigated by the methods of electron emission and ion reception [30]. Electron emission is the method in which a device pulls (or draws) electrons from the spacecraft ground and ejects them into space, while the ion reception is the method in which positive ions arrive at a spacecraft that is negatively charged to neutralize the negative charges. The former method is effective for reducing the negative charge of the spacecraft ground but not effective for dielectric surfaces. As a demerit, the process can lead to differential charging between the dielectric and the conducting ground. The later method is effective for mitigating negatively charged surface (whether dielectric or conductor), and reducing differential charging. However, it has the disadvantage of electroplating the entire spacecraft with extended use. Because each method has advantage (or disadvantage) over the other, the use of a combination of both types has been recommended. Other mitigation methods include plasma emission, partially conducting paint, polar molecule emission, mirror reflection and violet irradiation [31].

**Figure 7.** *Regions of concern for internal charging of spacecrafts in space (source: Ref. [29]).*

#### **5.2 Single event effects**

For memories and data related devices, some of the error mitigation approach or methods include Parity check, cyclic-redundancy check (CRC) coding, Hamming code, Reed-Solomon (R-S) coding, convolutional encoding and overlying protocol (see: Ref. [32] and references therein). Parity is a single bit added to the end of a data structure, such that it states whether an odd or even number of 'ones' was in the structure. The parity method counts the number of logic-one states or 'ones' that are occurring in a data path. The CRC coding method detects if any errors occurred in a given data structure based on performing modulo-two arithmetic operations on a given stream of data, and interpreting the results as a polynomial. The hamming code method detects the position of a single error and the existence of more than one error in a data structure. The R-S code can detect and correct multiple and consecutive errors in a data structure. The convolutional encoding can also detect and correct multiple bit errors. However, it is distinguishable from block coding (e.g., R-S code) by interleave of the overhead or check bits into the actual stream of data instead of being grouped into separate words at the end of the data structure. Errors in the control-related devices can also be mitigated using some of the above mentioned methods. A more effective mitigation approach for controlrelated devices with complex difficulties (e.g., large scale integration circuitry or microprocessors) is the software-based mitigation, which includes tasks or subroutines dubbed health and safety (H&S). The H&S tasks can perform memory scrubbing that utilizes parity or other method on either external memory devices or registers that are internal to the microprocessor. In the software mitigation methods, the internal microprocessor timers can also be used to operate a watchdog timer or for passing H&S messages between spacecraft systems (see: Ref. [32] for more detail).

#### **5.3 Total ionizing dose**

TID on satellites system can be mitigated by methods such as shielding, derating and conservative circuit design [33]. Shielding is the processes of protecting spacecraft (and the occupants) from ionizing radiation using a configuration of appropriate massive materials. Derating refers to techniques usually employed in electrical power and electronic devices in which devices are operated at maximum power dissipation that is less than their rated value, with consideration of the case or body temperature, ambient temperature and the type of cooling mechanism used. This method can increase the safety margin between part design limits and applied stresses, consequently enhancing protection of the part [34]. Hardening of critical components in satellites at design level is also a viable method. This has, however, been the practice of satellite manufacturers. These methods can also be used to mitigate *Displacement damage* because DD is similar to TID as the effect is also cumulative [33].

Other important mitigation approach includes the development of appropriate environmental model that can mimic the perturbed scenarios that are expected under extreme space environmental condition. A well-accomplished or more sophisticated model should account for the individual effects of various solar forcing mechanisms, which cause fluctuations in neutral and ionized density [35]. One other very important mitigation approach to consider is the development of extensive warning system for solar energetic events. Although solar activity can be predicted days in advance but ascertaining their level of impact on the satellite and the Earth environment is quite challenging. Therefore, effective monitoring of solar activity is essential in order to be able to predict atmospheric or ionospheric

**85**

shown in **Figure 8**.

**Figure 8.**

**6. Conclusion**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

responses to solar events and their consequence on satellite in orbit. In all, orbit consideration (and satellite's trajectory) is also important. Satellites in medium Earth orbit (MEO) and geostationary orbit (GEO) are subject to impacts of outer Van Allen radiation belt. LEO satellites encounter the most intense particle fluxes in the SAA [36], which is considered to be the main region where spacecrafts receive the largest fraction of the radiation exposure during spaceflight missions. The schematic diagram of Earth's radiation belts and their space weather concerns is

*Schematic diagram of Earth's radiation belts and their space weather concerns (from Ref. [36]).*

The space radiation environment driven by solar activity (and galactic cosmic rays) poses potent and unequivocal treat to satellites in near-Earth space. Understanding atmospheric and ionospheric dynamic responses to solar-driven particles and radiation, and their space weather implications are critical and of practical importance to satellites design and operation. The specific effects of radiation environment on as satellite depends on the source, type and energy of incident particle, as well as the satellite's orbit and/or position at the time of solar energetic events. Radiation mitigation measures can increase the safety margin between part design limits and the applied stresses resulting from particles impact, consequently enhancing protection of the part. However, it is important that the solar maximum phase be given more consideration in all mitigation effort because the rate of impact is higher during this interval. Severe solar storms can occur during the solar maximum that can produce huge short-lived increase in radiation levels, as well as high levels of SEEs that current mitigation measures might not be able to bear [37]. Also as dependence on satellites services increase, the economic and societal risk associated with space weather also increases, and likely impact can be unprecedented. In view of this, a contingency plans that include the possibility of switching to or benefitting from other independent satellite services have been recommended [8]. The upcoming multi-constellation GNSS receivers can play a significant role in this regard, such that the individual GNSS receivers will be inherently robust to a satellite service denial. Space weather-induced enhancement of atmospheric drag on satellites and consequent accelerated orbit decay is also a major perturbing force to reckon with, for satellites in low Earth orbit [35, 38–42]. A concise review of the impact and mitigation of this phenomenon will be published in the future. We note

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

**Figure 8.** *Schematic diagram of Earth's radiation belts and their space weather concerns (from Ref. [36]).*

responses to solar events and their consequence on satellite in orbit. In all, orbit consideration (and satellite's trajectory) is also important. Satellites in medium Earth orbit (MEO) and geostationary orbit (GEO) are subject to impacts of outer Van Allen radiation belt. LEO satellites encounter the most intense particle fluxes in the SAA [36], which is considered to be the main region where spacecrafts receive the largest fraction of the radiation exposure during spaceflight missions. The schematic diagram of Earth's radiation belts and their space weather concerns is shown in **Figure 8**.

#### **6. Conclusion**

*Satellites Missions and Technologies for Geosciences*

For memories and data related devices, some of the error mitigation approach or methods include Parity check, cyclic-redundancy check (CRC) coding, Hamming code, Reed-Solomon (R-S) coding, convolutional encoding and overlying protocol (see: Ref. [32] and references therein). Parity is a single bit added to the end of a data structure, such that it states whether an odd or even number of 'ones' was in the structure. The parity method counts the number of logic-one states or 'ones' that are occurring in a data path. The CRC coding method detects if any errors occurred in a given data structure based on performing modulo-two arithmetic operations on a given stream of data, and interpreting the results as a polynomial. The hamming code method detects the position of a single error and the existence of more than one error in a data structure. The R-S code can detect and correct multiple and consecutive errors in a data structure. The convolutional encoding can also detect and correct multiple bit errors. However, it is distinguishable from block coding (e.g., R-S code) by interleave of the overhead or check bits into the actual stream of data instead of being grouped into separate words at the end of the data structure. Errors in the control-related devices can also be mitigated using some of the above mentioned methods. A more effective mitigation approach for controlrelated devices with complex difficulties (e.g., large scale integration circuitry or microprocessors) is the software-based mitigation, which includes tasks or subroutines dubbed health and safety (H&S). The H&S tasks can perform memory scrubbing that utilizes parity or other method on either external memory devices or registers that are internal to the microprocessor. In the software mitigation methods, the internal microprocessor timers can also be used to operate a watchdog timer or for passing H&S messages between spacecraft systems (see: Ref. [32] for

TID on satellites system can be mitigated by methods such as shielding, derating and conservative circuit design [33]. Shielding is the processes of protecting spacecraft (and the occupants) from ionizing radiation using a configuration of appropriate massive materials. Derating refers to techniques usually employed in electrical power and electronic devices in which devices are operated at maximum power dissipation that is less than their rated value, with consideration of the case or body temperature, ambient temperature and the type of cooling mechanism used. This method can increase the safety margin between part design limits and applied stresses, consequently enhancing protection of the part [34]. Hardening of critical components in satellites at design level is also a viable method. This has, however, been the practice of satellite manufacturers. These methods can also be used to mitigate *Displacement damage* because DD is similar to TID as the effect is

Other important mitigation approach includes the development of appropriate environmental model that can mimic the perturbed scenarios that are expected under extreme space environmental condition. A well-accomplished or more sophisticated model should account for the individual effects of various solar forcing mechanisms, which cause fluctuations in neutral and ionized density [35]. One other very important mitigation approach to consider is the development of extensive warning system for solar energetic events. Although solar activity can be predicted days in advance but ascertaining their level of impact on the satellite and the Earth environment is quite challenging. Therefore, effective monitoring of solar activity is essential in order to be able to predict atmospheric or ionospheric

**5.2 Single event effects**

more detail).

**5.3 Total ionizing dose**

also cumulative [33].

**84**

The space radiation environment driven by solar activity (and galactic cosmic rays) poses potent and unequivocal treat to satellites in near-Earth space. Understanding atmospheric and ionospheric dynamic responses to solar-driven particles and radiation, and their space weather implications are critical and of practical importance to satellites design and operation. The specific effects of radiation environment on as satellite depends on the source, type and energy of incident particle, as well as the satellite's orbit and/or position at the time of solar energetic events. Radiation mitigation measures can increase the safety margin between part design limits and the applied stresses resulting from particles impact, consequently enhancing protection of the part. However, it is important that the solar maximum phase be given more consideration in all mitigation effort because the rate of impact is higher during this interval. Severe solar storms can occur during the solar maximum that can produce huge short-lived increase in radiation levels, as well as high levels of SEEs that current mitigation measures might not be able to bear [37]. Also as dependence on satellites services increase, the economic and societal risk associated with space weather also increases, and likely impact can be unprecedented. In view of this, a contingency plans that include the possibility of switching to or benefitting from other independent satellite services have been recommended [8]. The upcoming multi-constellation GNSS receivers can play a significant role in this regard, such that the individual GNSS receivers will be inherently robust to a satellite service denial. Space weather-induced enhancement of atmospheric drag on satellites and consequent accelerated orbit decay is also a major perturbing force to reckon with, for satellites in low Earth orbit [35, 38–42]. A concise review of the impact and mitigation of this phenomenon will be published in the future. We note

that this review (on the space radiation effects on satellites and their mitigation methods) is succinct when compared to the large body of work in the subject area. Therefore, we encourage readers to also consult other well-accomplished texts for specific space radiation effect and the appropriate mitigation approach.

### **Author details**

Victor U.J. Nwankwo1 \*, Nnamdi N. Jibiri2 and Michael T. Kio3

1 Space, Atmospheric Physics and Radio Wave Propagation Laboratory, Department of Physics, Anchor University, Lagos, Nigeria

2 Radiation and Health Physics Laboratory, Department of Physics, University of Ibadan, Ibadan, Nigeria

3 Engineering and Space Systems Department, National Space and Research Development Agency, Abuja, Nigeria

\*Address all correspondence to: vnwankwo@aul.edu.ng

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**87**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

2010

Resource; 2019

10.17226/24993

Infrastructure. London: Royal Academy

[9] Allen JH, Wilkinson D. Spacecraft charging: Then and now. In: Spacecraft charging technology conference, Albuquerque, NM, 20-24 September

[10] National Weather Service (NWS).

[11] The National Academies of Sciences, Engineering and Medicine. Testing at the speed of light: The State of U.S. In: Electronic Parts Space Radiation Testing Infrastructure. Washington, DC: The National Academic Press; 2018. DOI:

[12] Holbert KE. Space Radiation Environmental Effects. Courses in Electrical Engineering, Arizona State University. 2007. Available from: http:// holbert.faculty.asu.edu/eee560/spacerad. html [Retrieved: December 2010]

[13] Prolss GW. Physics of the Earth's Space Environment. Berlin, Heidelberg,

[14] Gopalswamy N. Coronal mass ejections and space weather. In: Tsuda T, Fuji R, Shibata K, Geller MA, editors. Climate and Weather of the Sun-Earth System (CAWSES): Selected Paper from the 2007 Kyoto Symposium; 2009.

[15] Mewalt RA. Cosmic Ray. 1996. Available from: http://www.srl.caltech. edu/personnel/dick/cos\_encyc.html

[16] Adams L. Space Radiation Effects in Electronic Components. PA and

Germany: Springer; 2004

pp. 77-120

[Retrieved: July 2019]

Solar experts predict the Sun's activity in Solar Cycle 25 to be below average, similar to Solar Cycle 24. National Oceanic and Atmospheric Administration (NOAA). Online

of Engineering (RAE); 2013

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

[1] Nwankwo VJN. Determination of the stopping power and failure time of spacecraft components due to proton (e+) interaction using GOES 11 acquisition data [M.Sc. thesis]. Ibadan, Nigeria: University of Ibadan; 2010

[2] Jibiri NN, Nwankwo VUJ, Kio M. Determination of the stopping power and failure time of spacecraft components due to proton interaction using GOES 11 acquisition data. International Journal of Engineering, Science and Technology.

[3] Iucci N, Levitin AE, Belov AV,

[4] Shea MA, Smart DF, Allen JH, Wilkinson DL. Spacecraft problems in association with episodes of intense solar activity and related terrestrial phenomena during March 1991. IEEE Transactions on Nuclear Science.

[5] Bedingfield KL, Leach RD, Alexander MB, editors. Spacecraft System Failures and Anomalies Attributed to Natural Space

MSFC, Alabama 35812; 1996

Weather. 2015;**13**:484-502

[8] RAE. Extreme Space Weather: Impacts on Engineered Systems and

Environment. NASA REF-1390. NASA-

[6] Martin G. SumbandilaSat beyond repair. Defence Web. 2012. Available from: https://www.defenceweb.co.za/ joint/science-a-defence-technology/ sumbandilasat-beyond-repair/ [Retreived: 20 May 2019]

[7] Loto'aniu TM, Singer HJ, Rodriguez JV, Green J, Denig W, Biesecker D, et al. Space weather conditions during the Galaxy 15 spacecraft anomaly. Space

Eroshenko EA, Ptitsyna NG, Villoresi G, et al. Space weather conditions and spacecraft anomalies in different orbits. Space Weather. 2005;**3**:1-16. S01001

**References**

2011;**3**:6532-6542

1992;**39**:1754-1760

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

#### **References**

*Satellites Missions and Technologies for Geosciences*

that this review (on the space radiation effects on satellites and their mitigation methods) is succinct when compared to the large body of work in the subject area. Therefore, we encourage readers to also consult other well-accomplished texts for

specific space radiation effect and the appropriate mitigation approach.

**86**

**Author details**

Victor U.J. Nwankwo1

Ibadan, Ibadan, Nigeria

\*, Nnamdi N. Jibiri2

of Physics, Anchor University, Lagos, Nigeria

\*Address all correspondence to: vnwankwo@aul.edu.ng

Development Agency, Abuja, Nigeria

provided the original work is properly cited.

and Michael T. Kio3

1 Space, Atmospheric Physics and Radio Wave Propagation Laboratory, Department

2 Radiation and Health Physics Laboratory, Department of Physics, University of

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

3 Engineering and Space Systems Department, National Space and Research

[1] Nwankwo VJN. Determination of the stopping power and failure time of spacecraft components due to proton (e+) interaction using GOES 11 acquisition data [M.Sc. thesis]. Ibadan, Nigeria: University of Ibadan; 2010

[2] Jibiri NN, Nwankwo VUJ, Kio M. Determination of the stopping power and failure time of spacecraft components due to proton interaction using GOES 11 acquisition data. International Journal of Engineering, Science and Technology. 2011;**3**:6532-6542

[3] Iucci N, Levitin AE, Belov AV, Eroshenko EA, Ptitsyna NG, Villoresi G, et al. Space weather conditions and spacecraft anomalies in different orbits. Space Weather. 2005;**3**:1-16. S01001

[4] Shea MA, Smart DF, Allen JH, Wilkinson DL. Spacecraft problems in association with episodes of intense solar activity and related terrestrial phenomena during March 1991. IEEE Transactions on Nuclear Science. 1992;**39**:1754-1760

[5] Bedingfield KL, Leach RD, Alexander MB, editors. Spacecraft System Failures and Anomalies Attributed to Natural Space Environment. NASA REF-1390. NASA-MSFC, Alabama 35812; 1996

[6] Martin G. SumbandilaSat beyond repair. Defence Web. 2012. Available from: https://www.defenceweb.co.za/ joint/science-a-defence-technology/ sumbandilasat-beyond-repair/ [Retreived: 20 May 2019]

[7] Loto'aniu TM, Singer HJ, Rodriguez JV, Green J, Denig W, Biesecker D, et al. Space weather conditions during the Galaxy 15 spacecraft anomaly. Space Weather. 2015;**13**:484-502

[8] RAE. Extreme Space Weather: Impacts on Engineered Systems and Infrastructure. London: Royal Academy of Engineering (RAE); 2013

[9] Allen JH, Wilkinson D. Spacecraft charging: Then and now. In: Spacecraft charging technology conference, Albuquerque, NM, 20-24 September 2010

[10] National Weather Service (NWS). Solar experts predict the Sun's activity in Solar Cycle 25 to be below average, similar to Solar Cycle 24. National Oceanic and Atmospheric Administration (NOAA). Online Resource; 2019

[11] The National Academies of Sciences, Engineering and Medicine. Testing at the speed of light: The State of U.S. In: Electronic Parts Space Radiation Testing Infrastructure. Washington, DC: The National Academic Press; 2018. DOI: 10.17226/24993

[12] Holbert KE. Space Radiation Environmental Effects. Courses in Electrical Engineering, Arizona State University. 2007. Available from: http:// holbert.faculty.asu.edu/eee560/spacerad. html [Retrieved: December 2010]

[13] Prolss GW. Physics of the Earth's Space Environment. Berlin, Heidelberg, Germany: Springer; 2004

[14] Gopalswamy N. Coronal mass ejections and space weather. In: Tsuda T, Fuji R, Shibata K, Geller MA, editors. Climate and Weather of the Sun-Earth System (CAWSES): Selected Paper from the 2007 Kyoto Symposium; 2009. pp. 77-120

[15] Mewalt RA. Cosmic Ray. 1996. Available from: http://www.srl.caltech. edu/personnel/dick/cos\_encyc.html [Retrieved: July 2019]

[16] Adams L. Space Radiation Effects in Electronic Components. PA and

Safety Office, Brunel University. 2003. Available from: http://paso.esa. int/5\_training…/training\_03\_space%20 radiation.ppt [Retrieved: March 2010]

[17] Mikaelian T. Spacecraft Charging and Hazards to Electronics in Space. York University Publication; 2001. arXiv:0906.3884; 2009

[18] Shaw RR, Nanevicz JE, Adamo RC. Observation of electrical discharges caused by differential satellite charging by magnetospheric plasmas. In: Rosen A, editor. Spacecraft Charging by Magnetospheric Plasma. Progress in Astronautics and Aeronautics. Vol. 47. 1976. pp. 61-76

[19] Leach RD, Alexander MB. Failure and Anomalies Attributed to Spacecraft Charging. AL: NASA RP-1375 Marshall Space Flight Centre; 1995

[20] Gussenhoven MS, Mullen EG, Brautigam DH. Improved understanding of the Earth's radiation belts from the CRRES satellite. IEEE Transactions on Nuclear Science. 1996;**43**(2):353-368

[21] Sayyah R, Macleod TC, Ho FD. Radiation-hardened electronics and ferroelectric memory for space flight systems. Ferroelectrics. 2011;**413**:170-175

[22] Nichols DK, Coss JR, Watson RK, Schwartz HR, Pease RL. An observation of proton-induced latchup. IEEE Transactions on Nuclear Science. 1992;**39**(6):1654-1656

[23] Mouret I, Allenspach M, Schrimpf RD, Brews JR, Galloway KF, Calvel P. Temperature and angular dependence of substrate response in SEGR. IEEE Transactions on Nuclear Science. 1994;**41**(6):2216-2221

[24] Adams JH, Gelman A. The effects of solar flares on single event upset rates.

IEEE Transactions on Nuclear Science. 1984;**39**(6):1212-1216

[25] Campbell AB. SEU flight data from CRRES MEP. IEEE Transactions on Nuclear Science. 1991;**38**(6):1647-1654

[26] Oldham TR, McLean FB. Total ionizing dose in MOS oxides and devices. IEEE Transactions on Nuclear Science. 2003;**50**(3)

[27] Srour JR, Marshall CJ, Marshall PW. Review of displacement damage effects in silicon devices. IEEE Transactions on Nuclear Science. 2003;**50**:653-670

[28] Getachew A. Stopping Power and Range of Protons of Various Energies in Different Materials [M.Sc. thesis]. Ethiopia: Department of Physics, Addis Ababa University; 2007

[29] Garrett HB. Space Weather Impacts on Spacecrafts and Mitigation Strategies. California, USA, Pasadena, CA, USA: Jet Propulsion Laboratory, California Institute of Technology; 2012

[30] Lai ST. A critical overview on spacecraft charging control method. In: 6th Spacecraft Charging Technology Conference, AFRL-VS-TR-20001578; 2000

[31] Lai ST, Cahoy K. Spacecraft charging. In: Encyclopedia of Plasma Technology. Taylor & Francis; 2017. DOI: 10.1081/E-EPLT-120053644

[32] Maurer RH, Fraeman ME, Martin MN, Roth DR. Harsh environments: Space radiation environment, effects and mitigation. Johns Hopkins APL Technical Digest. 2008;**28**:17-29

[33] Shepherd SM. Spacecraft shielding. Online lecture series, Thayer Scholl of Engineering at Dartmouth College [Retrieved: October 2019]

**89**

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space*

[42] Nwankwo VUJ. Effects of space weather on Earth's ionosphere and nominal LEO satellites' aerodynamic drag [PhD thesis]. Kolkata, India: University of Calcutta; 2016

*DOI: http://dx.doi.org/10.5772/intechopen.90115*

[34] ReliaSoft Corporation. Reliability Basics. Reliability HotWire e-Magazine. HBM Prenscia Inc. Weibull.com;

[35] Nwankwo VUJ, Chakrabarti SK, Weigel RS. Effects of plasma drag on low earth orbiting satellites due to solar forcing induced perturbations and heating. Advances in Space Research.

[36] Baker DN, Erickson PJ, Fennell JF, Foster JC, Jaynes AN, Verronen PT. Space weather effects in the Earth's radiation belts. Space Science Reviews. 2017;**214**:17

[37] Hapgood and Thomson (for Lloyd's 360° Risk Insight). Space Weather: It's Impact on Earth and Implications for Business. London: RAL Space; 2010

[38] Nwankwo VUJ, Chakrabarti KS. Theoretical modeling of drag force impact on a model international space station (ISS) during variation of solar activity. Transactions of JSASS, Aerospace

Technology Japan. 2014;**12**:47-53

perturbations on trans-Martian trajectory of Mars missions before Mars orbit insertion. Indian Journal of

[40] Nwankwo VUJ, Chakrabarti S. Effects of space weather on the ionosphere and LEO satellites orbital trajectory in the equatorial, low and mid-latitude regions. Advances in Space

[41] Nwankwo VUJ. Space weather: Responses of the atmosphere to solar activity and its implications for LEO satellites aerodynamic drag. In: Mukhopadhyay B, Sasmal S, editors. Exploring the Universe: From Near Space to Extra-Galactic. Springer International Publishing, Springer Nature Switzerland AG; 2018

Physics. 2015;**89**:1235-1245

Research. 2018;**61**:1880-1889

[39] Nwankwo VUJ, Chakrabarti SK. Analysis of planetary and solar induced

2008

2015;**56**:47-56

*The Impact of Space Radiation Environment on Satellites Operation in Near-Earth Space DOI: http://dx.doi.org/10.5772/intechopen.90115*

[34] ReliaSoft Corporation. Reliability Basics. Reliability HotWire e-Magazine. HBM Prenscia Inc. Weibull.com; 2008

*Satellites Missions and Technologies for Geosciences*

IEEE Transactions on Nuclear Science.

[25] Campbell AB. SEU flight data from CRRES MEP. IEEE Transactions on Nuclear Science. 1991;**38**(6):1647-1654

[26] Oldham TR, McLean FB. Total ionizing dose in MOS oxides and devices. IEEE Transactions on Nuclear

Marshall PW. Review of displacement damage effects in silicon devices. IEEE Transactions on Nuclear Science.

[28] Getachew A. Stopping Power and Range of Protons of Various Energies in Different Materials [M.Sc. thesis]. Ethiopia: Department of Physics, Addis

1984;**39**(6):1212-1216

Science. 2003;**50**(3)

2003;**50**:653-670

Ababa University; 2007

2000

[29] Garrett HB. Space Weather Impacts on Spacecrafts and Mitigation Strategies. California, USA, Pasadena, CA, USA: Jet Propulsion Laboratory, California Institute of Technology; 2012

[30] Lai ST. A critical overview on spacecraft charging control method. In: 6th Spacecraft Charging Technology Conference, AFRL-VS-TR-20001578;

[31] Lai ST, Cahoy K. Spacecraft charging. In: Encyclopedia of Plasma Technology. Taylor & Francis; 2017. DOI: 10.1081/E-EPLT-120053644

[32] Maurer RH, Fraeman ME, Martin MN, Roth DR. Harsh environments: Space radiation environment, effects and mitigation. Johns Hopkins APL Technical Digest.

[Retrieved: October 2019]

[33] Shepherd SM. Spacecraft shielding. Online lecture series, Thayer Scholl of Engineering at Dartmouth College

2008;**28**:17-29

[27] Srour JR, Marshall CJ,

Safety Office, Brunel University. 2003. Available from: http://paso.esa. int/5\_training…/training\_03\_space%20 radiation.ppt [Retrieved: March 2010]

[17] Mikaelian T. Spacecraft Charging and Hazards to Electronics in Space. York University Publication; 2001.

Adamo RC. Observation of electrical discharges caused by differential satellite charging by magnetospheric plasmas. In: Rosen A, editor. Spacecraft

Plasma. Progress in Astronautics and Aeronautics. Vol. 47. 1976. pp. 61-76

[19] Leach RD, Alexander MB. Failure and Anomalies Attributed to Spacecraft Charging. AL: NASA RP-1375 Marshall

[20] Gussenhoven MS, Mullen EG,

[21] Sayyah R, Macleod TC, Ho FD. Radiation-hardened electronics and ferroelectric memory for space flight systems. Ferroelectrics.

[22] Nichols DK, Coss JR, Watson RK, Schwartz HR, Pease RL. An observation of proton-induced latchup. IEEE Transactions on Nuclear Science.

Schrimpf RD, Brews JR, Galloway KF, Calvel P. Temperature and angular dependence of substrate response in SEGR. IEEE Transactions on Nuclear Science. 1994;**41**(6):2216-2221

[24] Adams JH, Gelman A. The effects of solar flares on single event upset rates.

understanding of the Earth's radiation belts from the CRRES satellite. IEEE Transactions on Nuclear Science.

arXiv:0906.3884; 2009

[18] Shaw RR, Nanevicz JE,

Charging by Magnetospheric

Space Flight Centre; 1995

Brautigam DH. Improved

1996;**43**(2):353-368

2011;**413**:170-175

1992;**39**(6):1654-1656

[23] Mouret I, Allenspach M,

**88**

[35] Nwankwo VUJ, Chakrabarti SK, Weigel RS. Effects of plasma drag on low earth orbiting satellites due to solar forcing induced perturbations and heating. Advances in Space Research. 2015;**56**:47-56

[36] Baker DN, Erickson PJ, Fennell JF, Foster JC, Jaynes AN, Verronen PT. Space weather effects in the Earth's radiation belts. Space Science Reviews. 2017;**214**:17

[37] Hapgood and Thomson (for Lloyd's 360° Risk Insight). Space Weather: It's Impact on Earth and Implications for Business. London: RAL Space; 2010

[38] Nwankwo VUJ, Chakrabarti KS. Theoretical modeling of drag force impact on a model international space station (ISS) during variation of solar activity. Transactions of JSASS, Aerospace Technology Japan. 2014;**12**:47-53

[39] Nwankwo VUJ, Chakrabarti SK. Analysis of planetary and solar induced perturbations on trans-Martian trajectory of Mars missions before Mars orbit insertion. Indian Journal of Physics. 2015;**89**:1235-1245

[40] Nwankwo VUJ, Chakrabarti S. Effects of space weather on the ionosphere and LEO satellites orbital trajectory in the equatorial, low and mid-latitude regions. Advances in Space Research. 2018;**61**:1880-1889

[41] Nwankwo VUJ. Space weather: Responses of the atmosphere to solar activity and its implications for LEO satellites aerodynamic drag. In: Mukhopadhyay B, Sasmal S, editors. Exploring the Universe: From Near Space to Extra-Galactic. Springer International Publishing, Springer Nature Switzerland AG; 2018

[42] Nwankwo VUJ. Effects of space weather on Earth's ionosphere and nominal LEO satellites' aerodynamic drag [PhD thesis]. Kolkata, India: University of Calcutta; 2016

**Chapter 6**

**Abstract**

Needs and Tricks

*Shishir Priyadarshi*

measure of scintillation indices.

**1. Introduction**

**91**

**Keywords:** ionospheric scintillation, empirical scintillation model, GPS/GNSS scintillation modeling, global scintillation model

Whenever the radio-wave signals pass through the ionospheric irregularities, these signals feel reflection, refraction, and scintillations (i.e., sharp and rapid carrier-phase variations and signal-to-noise ratio fading). In general it is more typical when the carrier-phase loss of lock happens due to the sharp signal-to-noise ratio fading ([1, 2] and references therein). As the wave propagates to the ground, scintillation models are needed to produce global as well as local ionospheric scintillation data and maps during the required solar activity condition, day, season, and geographic locations. Generally, scintillation models are made to serve special needs and not for all the spatial and temporal situations; due to this generally each scintil-

lation model is not fit for all the geographic and solar activity conditions.

In general, scintillation models often have two significant limitations; firstly physics-based models often fail in producing scintillation morphology during

Ionospheric Scintillation Modeling

The wavelength of the radio-wave satellite signal is of the order of the minimal small-scale ionospheric irregularities (i.e., a few centimeters). As the satellite signal passes through the ionosphere, its interaction with the ionospheric irregularity structures causes refraction, reflection, and polarization in the satellite signal. Ionospheric irregularities degrade the trans-ionospheric radio-wave signal quality, between the satellite and the receivers, due to scintillation. The physics-based model often fails to produce global morphology during the extreme solar events, whereas empirical models based on the ionospheric scintillation data demonstrate better quality to forecast the scintillation effects during extreme solar event. It is really tricky to make a scintillation model that is sensitive to low and high solar activities as well as extreme solar events simultaneously. In the presented book chapter, we will discuss/review the needs and tricks of modeling ionospheric scintillation during extreme solar events as well as all weather and latitudinal cases. There are several aspects that influence the scintillation occurrence, its strength, and global distribution. The latitudinal dependence, local weather, solar/geomagnetic activity conditions, and local times are the widely accepted factors that control and influence ionospheric scintillation most. This book chapter discusses all these aspects and also suggests the ways to cast aside those factors that led to the wrong

#### **Chapter 6**

## Ionospheric Scintillation Modeling Needs and Tricks

*Shishir Priyadarshi*

#### **Abstract**

The wavelength of the radio-wave satellite signal is of the order of the minimal small-scale ionospheric irregularities (i.e., a few centimeters). As the satellite signal passes through the ionosphere, its interaction with the ionospheric irregularity structures causes refraction, reflection, and polarization in the satellite signal. Ionospheric irregularities degrade the trans-ionospheric radio-wave signal quality, between the satellite and the receivers, due to scintillation. The physics-based model often fails to produce global morphology during the extreme solar events, whereas empirical models based on the ionospheric scintillation data demonstrate better quality to forecast the scintillation effects during extreme solar event. It is really tricky to make a scintillation model that is sensitive to low and high solar activities as well as extreme solar events simultaneously. In the presented book chapter, we will discuss/review the needs and tricks of modeling ionospheric scintillation during extreme solar events as well as all weather and latitudinal cases. There are several aspects that influence the scintillation occurrence, its strength, and global distribution. The latitudinal dependence, local weather, solar/geomagnetic activity conditions, and local times are the widely accepted factors that control and influence ionospheric scintillation most. This book chapter discusses all these aspects and also suggests the ways to cast aside those factors that led to the wrong measure of scintillation indices.

**Keywords:** ionospheric scintillation, empirical scintillation model, GPS/GNSS scintillation modeling, global scintillation model

#### **1. Introduction**

Whenever the radio-wave signals pass through the ionospheric irregularities, these signals feel reflection, refraction, and scintillations (i.e., sharp and rapid carrier-phase variations and signal-to-noise ratio fading). In general it is more typical when the carrier-phase loss of lock happens due to the sharp signal-to-noise ratio fading ([1, 2] and references therein). As the wave propagates to the ground, scintillation models are needed to produce global as well as local ionospheric scintillation data and maps during the required solar activity condition, day, season, and geographic locations. Generally, scintillation models are made to serve special needs and not for all the spatial and temporal situations; due to this generally each scintillation model is not fit for all the geographic and solar activity conditions.

In general, scintillation models often have two significant limitations; firstly physics-based models often fail in producing scintillation morphology during

extreme solar and geomagnetic events. The second limitation is there is no possibility of a correction in the model once its algorithm is derived. In the presented book chapter, scintillation empirical modeling methods and their limitations are discussed. Geometrical effects contaminate scintillation observations most. We have discussed in this chapter how to overcome scintillation modeling limitations and use some tricks that replicate the actual scintillation morphology. The presented text in this chapter will enrich the knowledge of ionospheric modelers and make them understand how beginners should proceed with the ionospheric scintillation or ionospheric electron density as a model input data, if they decided to model certain ionospheric parameters (such as spectral index, turbulence strength parameters of the ionospheric irregularities, amplitude and phase fluctuations, scintillation indices, etc.).

should always keep in mind that their algorithm should be sensitive to the geographic locations, solar activity as well as local weather. This can be achieved by having different derivation algorithm for each and every geographic location, and the final algorithm should combine all these sub-algorithm to demonstrate the

It is better to have the latest data for modeling new events, but, in case of new data unavailability, the previous data may be from the previous solar cycle [6]. The latest data are generally close to the new solar/or geomagnetic event, and they are from the same solar cycle activity period. Therefore, the scintillation models based on the latest data are comparatively closer to the new observations in comparison to the data from the previous solar cycles. The advent of scintillation effect on the trans-ionospheric radio-wave signal lower elevation angles (≤ 20°) is being

discarded from the data to minimize the effect of multipath. Multipath can occur on any elevation angle. To reduce the high-elevation angle, multipath scintillation receiver's antenna must be setup to minimize CODE and PHASE reflections

(multipath), by mounting it away from close reflecting surfaces [6]. The multipath effect also depends on the PRN code rate (please see [7] for details). Moreover the multipath effect can appear at higher elevation angles than 20° (please see [8] for details). Multipath effects occurring at any elevation can be minimized by the geometrical corrections in the data [2]. But, to do it one should be well familiar with the other high-altitude structures (such as mountains or high-altitude building, etc.) near the GPS/GNSS receivers' location and filter the datasets reflected from such structures to reduce the multipath effect. To avoid the high-altitude structures, we have to manual check the data and avoid those elevation angles which show unusual enhancement in the scintillation indices. Following Rino [9], ionospheric irregularities orient themselves according to the local geomagnetic field lines. The phase lock loss highly depends on the angle between the geomagnetic field lines and receiver-transmitter line of sight [10, 11]. For example, at the polar ionosphere, ionospheric irregularities often form rodlike structure and orient themselves along the vertical geomagnetic field lines, whereas at the mid and equatorial latitudes, the local geomagnetic field line is merely horizontal to the Earth's surface due to which sheet- or winglike ionospheric irregularity structures frequently appear. If the irregularity orientations are along the geomagnetic field lines, they form a rodlike structure; if the ionospheric irregularity orientation is across the geomagnetic field lines, it may appear either as a wing- or as a sheetlike structure. We are addressing both cases (rodlike structure and the high-latitude and sheet-/winglike structure at

Following Rino [9] and Booker [12], phase and amplitude scintillations can be

where re is the classical electron radius; λ is the wavelength of the signal; L is the

*q*�2*ϑ*þ<sup>1</sup> <sup>0</sup> <sup>Γ</sup> *<sup>υ</sup>* � <sup>1</sup>

> <sup>4</sup>*πΓ υ* <sup>þ</sup> <sup>1</sup> 2

> > 2 � �

2 � �

*<sup>π</sup>* <sup>p</sup> <sup>Γ</sup>ð Þ ð Þ *<sup>υ</sup>* <sup>þ</sup> <sup>0</sup>*:*<sup>5</sup> *<sup>=</sup>*<sup>2</sup> ð Þ *<sup>υ</sup>* � <sup>0</sup>*:*<sup>5</sup> *<sup>ℊ</sup> F a*ð Þ *; <sup>b</sup>* (2)

� � *F a*ð Þ *; <sup>b</sup>* (1)

ð Þ *Lsecθ GCs*

2 ffiffiffi

irregularity slab thickness; θ is the satellite zenith angle; Cs is the turbulence strength parameter, which is a function of the fluctuation in the electron density (ΔN/N) in the irregularity slab along the satellite signal path; G is the geometric

ð Þ *Lsec<sup>θ</sup> CsZ<sup>υ</sup>*�1*=*<sup>2</sup> <sup>Γ</sup> <sup>2</sup>*:*5�*<sup>υ</sup>*

**2.2 Geometrical error correction and lower elevation multipath**

the equatorial and mid-latitude) in this book chapter.

> ¼ *r* 2 *e λ*2

< *δφ*<sup>2</sup>

expressed as follows:

*S*2 <sup>4</sup> ¼ *r* 2 *e λ*2

**93**

global scintillation response.

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

#### **2. Scintillation modeling tricks and correction for the geometry of propagation**

As the elevation angle of the signal wave source changes, we observe the changes in the intensity of ionospheric scintillation. Such changes are significant as they are caused due to enhancement in the path of the radio-wave signal through the ionospheric irregularity layer with decreasing elevation angle and vice versa [3]. The signal ray path through the ionospheric irregularity also depends on the size and orientation of the ionospheric irregularity structure along with the height and thickness of the ionospheric irregularity layer [1]. All these elements influence the intensity of the ionospheric scintillation, and the effect caused by them on the transionospheric radio-wave is termed as the geometrical effect [3–5]. In this section, we will cover the scintillation modeling tricks and method to minimize the multipath effect and impact ways to overcome other geometrical effects such as irregularity orientation with respect to the local geomagnetic field lines, multipath at higher elevation angle, radio-wave signals' extended path near the Earth's horizon, etc.

#### **2.1 Scintillation modeling tricks**

For the empirical scintillation modeling, first, we should need to have data for all the seasons and solar as well as geomagnetic activity situation. For instance, if we are planning to model ionospheric scintillation during high solar activity in winter months, we must have winter-month data collected during high solar activity period. Similarly, for the low solar activity period and in particular month scintillation modeling, we must have alike data. An adequate empirical modeling study expands understanding of ionospheric irregularities and can be used to better evaluate the impact of scintillation on different resources. The physics-based model often fails to produce global morphology during extreme solar events, whereas empirical models based on the ionospheric scintillation data demonstrate better scintillation effects during extreme solar events. It is really tricky to make a scintillation model which is sensitive to low and high solar activities as well as extreme solar events simultaneously.

The modeler should also derive the relationship between solar activity and provisional activity indices with the ionospheric scintillation data in order to make the scintillation data sensitive to the solar activity condition. One should always keep in mind that all the solar activity, geomagnetic, and provisional indices are not equally beneficial to all the geographic locations. Therefore, one should use the proper geo, solar, and provisional activity indices for specific geographic locations. If the modeler is planning to develop a global ionospheric scintillation model, they

extreme solar and geomagnetic events. The second limitation is there is no possibility of a correction in the model once its algorithm is derived. In the presented book

chapter, scintillation empirical modeling methods and their limitations are discussed. Geometrical effects contaminate scintillation observations most. We have discussed in this chapter how to overcome scintillation modeling limitations and use some tricks that replicate the actual scintillation morphology. The presented text in this chapter will enrich the knowledge of ionospheric modelers and make them understand how beginners should proceed with the ionospheric scintillation or ionospheric electron density as a model input data, if they decided to model certain ionospheric parameters (such as spectral index, turbulence strength parameters of the ionospheric irregularities, amplitude and phase fluctuations,

**2. Scintillation modeling tricks and correction for the geometry**

As the elevation angle of the signal wave source changes, we observe the changes in the intensity of ionospheric scintillation. Such changes are significant as they are caused due to enhancement in the path of the radio-wave signal through the ionospheric irregularity layer with decreasing elevation angle and vice versa [3]. The signal ray path through the ionospheric irregularity also depends on the size and orientation of the ionospheric irregularity structure along with the height and thickness of the ionospheric irregularity layer [1]. All these elements influence the intensity of the ionospheric scintillation, and the effect caused by them on the transionospheric radio-wave is termed as the geometrical effect [3–5]. In this section, we will cover the scintillation modeling tricks and method to minimize the multipath effect and impact ways to overcome other geometrical effects such as irregularity orientation with respect to the local geomagnetic field lines, multipath at higher elevation angle, radio-wave signals' extended path near the Earth's horizon, etc.

For the empirical scintillation modeling, first, we should need to have data for all the seasons and solar as well as geomagnetic activity situation. For instance, if we are planning to model ionospheric scintillation during high solar activity in winter months, we must have winter-month data collected during high solar activity period. Similarly, for the low solar activity period and in particular month scintillation modeling, we must have alike data. An adequate empirical modeling study expands understanding of ionospheric irregularities and can be used to better evaluate the impact of scintillation on different resources. The physics-based model often fails to produce global morphology during extreme solar events, whereas empirical models based on the ionospheric scintillation data demonstrate better scintillation effects during extreme solar events. It is really tricky to make a scintillation model which is sensitive to low and high solar activities as well as

The modeler should also derive the relationship between solar activity and provisional activity indices with the ionospheric scintillation data in order to make the scintillation data sensitive to the solar activity condition. One should always keep in mind that all the solar activity, geomagnetic, and provisional indices are not equally beneficial to all the geographic locations. Therefore, one should use the proper geo, solar, and provisional activity indices for specific geographic locations. If the modeler is planning to develop a global ionospheric scintillation model, they

scintillation indices, etc.).

*Satellites Missions and Technologies for Geosciences*

**of propagation**

**2.1 Scintillation modeling tricks**

extreme solar events simultaneously.

**92**

should always keep in mind that their algorithm should be sensitive to the geographic locations, solar activity as well as local weather. This can be achieved by having different derivation algorithm for each and every geographic location, and the final algorithm should combine all these sub-algorithm to demonstrate the global scintillation response.

#### **2.2 Geometrical error correction and lower elevation multipath**

It is better to have the latest data for modeling new events, but, in case of new data unavailability, the previous data may be from the previous solar cycle [6]. The latest data are generally close to the new solar/or geomagnetic event, and they are from the same solar cycle activity period. Therefore, the scintillation models based on the latest data are comparatively closer to the new observations in comparison to the data from the previous solar cycles. The advent of scintillation effect on the trans-ionospheric radio-wave signal lower elevation angles (≤ 20°) is being discarded from the data to minimize the effect of multipath. Multipath can occur on any elevation angle. To reduce the high-elevation angle, multipath scintillation receiver's antenna must be setup to minimize CODE and PHASE reflections (multipath), by mounting it away from close reflecting surfaces [6]. The multipath effect also depends on the PRN code rate (please see [7] for details). Moreover the multipath effect can appear at higher elevation angles than 20° (please see [8] for details). Multipath effects occurring at any elevation can be minimized by the geometrical corrections in the data [2]. But, to do it one should be well familiar with the other high-altitude structures (such as mountains or high-altitude building, etc.) near the GPS/GNSS receivers' location and filter the datasets reflected from such structures to reduce the multipath effect. To avoid the high-altitude structures, we have to manual check the data and avoid those elevation angles which show unusual enhancement in the scintillation indices. Following Rino [9], ionospheric irregularities orient themselves according to the local geomagnetic field lines. The phase lock loss highly depends on the angle between the geomagnetic field lines and receiver-transmitter line of sight [10, 11]. For example, at the polar ionosphere, ionospheric irregularities often form rodlike structure and orient themselves along the vertical geomagnetic field lines, whereas at the mid and equatorial latitudes, the local geomagnetic field line is merely horizontal to the Earth's surface due to which sheet- or winglike ionospheric irregularity structures frequently appear. If the irregularity orientations are along the geomagnetic field lines, they form a rodlike structure; if the ionospheric irregularity orientation is across the geomagnetic field lines, it may appear either as a wing- or as a sheetlike structure. We are addressing both cases (rodlike structure and the high-latitude and sheet-/winglike structure at the equatorial and mid-latitude) in this book chapter.

Following Rino [9] and Booker [12], phase and amplitude scintillations can be expressed as follows:

$$<\delta\rho^2> = r\_\epsilon^2 \lambda^2 (Lsec\theta) G C\_t \frac{q\_0^{-2\theta+1} \Gamma\left(\nu - \frac{1}{2}\right)}{4\pi \Gamma\left(\nu + \frac{1}{2}\right)} F(a, b) \tag{1}$$

$$\mathbf{S}\_4^2 = r\_\epsilon^2 \lambda^2 (\mathbf{L} \sec \theta) \mathbf{C}\_\imath \mathbf{Z}^{\nu - 1/2} \frac{\Gamma \left( \frac{2.5 - \nu}{2} \right)}{2 \sqrt{\pi} \Gamma((\nu + 0.5)/2)(\nu - 0.5)} \, g \, F(a, b) \tag{2}$$

where re is the classical electron radius; λ is the wavelength of the signal; L is the irregularity slab thickness; θ is the satellite zenith angle; Cs is the turbulence strength parameter, which is a function of the fluctuation in the electron density (ΔN/N) in the irregularity slab along the satellite signal path; G is the geometric


on a moving object such as vehicles, ships, or airplanes; onboard at any beacon satellites; or on a spacecraft. The geometrical effect is highly dependent on the elevation angle at which the receiver receives the satellite signal, azimuth angle of the receiver, and orientation of the ionospheric irregularity structures with respect to the local geomagnetic field. The geometrical effects also depend on the angle between the signal ray path and the local geomagnetic field. Another important aspect that influences the scintillation morphology and the rate is the combination of several things such as the geographic location of our region of interest, local season, local time, solar activity condition, during a geomagnetic quite day or a geomagnetically disturbed day, etc. Here first we will discuss in details the geometrical influence on the ionospheric scintillation using the illustrative examples and demonstrate the influence of the geometrical correction on the satellite signal.

**3.1 Influence of the geometrical effects over the scintillation estimation and the**

In Eq. (1) the height of the ionospheric irregularity from the Earth's surface Z is a function of sec(θ). The zenith angle (θ) is a function of elevation angle (E) and

Using the relation between satellite and zenith angle, Eq. (2) can be simplified as

As we mentioned earlier, υ is a three-dimensional ionospheric irregularity spec-

S4 � sec 90 ð Þ � <sup>E</sup> sec 90 ð Þ � <sup>E</sup> <sup>υ</sup>�1*=*<sup>2</sup> h i<sup>1</sup>*=*<sup>2</sup>

dimensional spectral index as p = 2υ�1. If we simplify Eqs. (3) and (4), we will get to a direct dependence relationship between scintillation indices and zenith angle. Following Priyadarshi and Wernik [13], we can derive the spectral index p by using the log-log relationship of the scintillation index observation and cosecant of the

From Eq. (5) it is clear that the scintillation index is a power-law function of cosecant of the elevation angle with the power one-dimensional ionospheric irregularity spectral index (p) [13]. In order to simplify it more and avoid the dependence on the filter factor F(a,b), which is a complicated function of the ionospheric irregularity elongation parameters and which makes the overall ionospheric irregularity orientation dependence very complicated [13], we have considered the ionospheric radio-wave propagation environment as isotropic [9], and this turns F(a, b) = 1. Now if we plot log-log maps for S4 and Sin (E) angle, we can calculate the one-dimensional spectra index (p) from this relationship. Once we have spectral index, we can correct the scintillation for the geometry of propagation between the

**Figure 1** shows the non-corrected (S4\_observed) Vs-corrected scintillation indices (S4\_corrected) observed from the GPS scintillation receiver GSV 4004b deployed at

tral index [9]. One-dimensional spectral index (p) is related to the three-

θ ¼ 90 � E (3)

*<sup>S</sup>*<sup>4</sup> � cscð Þ *<sup>E</sup>* ð Þ *<sup>p</sup>*þ<sup>2</sup> *<sup>=</sup>*<sup>4</sup> F að Þ *;* <sup>b</sup> (5)

S4\_corrected <sup>¼</sup> S4\_observed*=*csc Eð Þð Þ <sup>P</sup>þ<sup>2</sup> *<sup>=</sup>*<sup>4</sup> (6)

(4)

**way to remove these errors**

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

can be expressed as

satellite elevation angle [13]:

**95**

receiver and the transmitter using the Eq. (6):

**Table 1.**

*Form of the ionospheric irregularities.*

factor and is a function of ionospheric irregularity elongation parameters F(a,b), "a" is the elongation parameter of the irregularities along the filed lines and "b" is used for elongation across the geomagnetic field lines; υ is the three-dimensional spectral index; q0 is the inner scale constant and Z ➔*λZRSec<sup>θ</sup>* <sup>4</sup>*<sup>π</sup>* ; ZR = ZZs/(Z + Zs) and Zs are the distance to the source; *ℊ* is the common geometry and propagation factor, which is also a function of elongation parameters a and b; and <*δφ*<sup>2</sup>> is the phase variance in the satellite signal after passing through the ionospheric irregularity.

Following the assumption of the theory of wave propagation in random medium, it is safe to assume at the signal frequency of interest that only the phase of the signal wave gets distorted [2] as the signal passes through the ionospheric irregularity of slab thickness L, and GPS scintillation receivers can observe the time series of the phase-modulated signal on the ground. Formulas (1) and (2) can be used to simulate the amplitude and phase scintillation. Study of the power spectrum of the ionospheric data improves the estimation of the local ionospheric irregularity form and their orientation with respect to the local geomagnetic field. Several ionospheric parameters such us spectral index, turbulence strength parameter, and phase fluctuations are essential for correcting the data contaminated through the geometrical errors.

Following Eq. (2) discussed in Rino [9], scintillation index is a function of Fresnel filter factor F(a,b) which is a function of elongation parameters "a" and "b." A detailed explanation of this function is discussed in Rino [9]; as it is beyond the scope of this book chapter therefore we are not discussing it in more details. But, for the reader's convenience, we are providing a summary table (please see **Table 1**), which summarizes the different combinations of "a" and "b" giving rise to different ionospheric irregularity shapes.

#### **3. Scintillation modeling needs and discussions**

Scintillation modeling provides a general scenario of the ionospheric scintillations' global morphology and occurrence during different solar activity and space weather conditions. It is always not possible to obtain ionospheric scintillation observation during some space weather events, and during such situations, a realistic ionospheric scintillation model can be used to fill the data gaps. On the other hand, data assimilation techniques also use scintillation model to assimilate scintillation observation into the scintillation model in order to improve the forecast.

There are two aspects that influence the occurrence and strength of the ionospheric scintillation. First is the geometrical effect between the radio-wave-emitting satellite and the receiver which can be at any different location, e.g., at the ground;

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

on a moving object such as vehicles, ships, or airplanes; onboard at any beacon satellites; or on a spacecraft. The geometrical effect is highly dependent on the elevation angle at which the receiver receives the satellite signal, azimuth angle of the receiver, and orientation of the ionospheric irregularity structures with respect to the local geomagnetic field. The geometrical effects also depend on the angle between the signal ray path and the local geomagnetic field. Another important aspect that influences the scintillation morphology and the rate is the combination of several things such as the geographic location of our region of interest, local season, local time, solar activity condition, during a geomagnetic quite day or a geomagnetically disturbed day, etc. Here first we will discuss in details the geometrical influence on the ionospheric scintillation using the illustrative examples and demonstrate the influence of the geometrical correction on the satellite signal.

#### **3.1 Influence of the geometrical effects over the scintillation estimation and the way to remove these errors**

In Eq. (1) the height of the ionospheric irregularity from the Earth's surface Z is a function of sec(θ). The zenith angle (θ) is a function of elevation angle (E) and can be expressed as

$$
\boldsymbol{\Theta} = \mathbf{\mathcal{O}} \mathbf{0} - \mathbf{E} \tag{3}
$$

Using the relation between satellite and zenith angle, Eq. (2) can be simplified as

$$\mathbf{S}\_4 \sim \left[ \sec \left( \mathbf{90} - \mathbf{E} \right) \sec \left( \mathbf{90} - \mathbf{E} \right)^{\mathbf{0} - 1/2} \right]^{1/2} \tag{4}$$

As we mentioned earlier, υ is a three-dimensional ionospheric irregularity spectral index [9]. One-dimensional spectral index (p) is related to the threedimensional spectral index as p = 2υ�1. If we simplify Eqs. (3) and (4), we will get to a direct dependence relationship between scintillation indices and zenith angle. Following Priyadarshi and Wernik [13], we can derive the spectral index p by using the log-log relationship of the scintillation index observation and cosecant of the satellite elevation angle [13]:

$$\mathbf{S}\_4 \sim \text{csc}(E)^{(p+2)/4} \text{ F}(\mathbf{a}, \mathbf{b}) \tag{5}$$

From Eq. (5) it is clear that the scintillation index is a power-law function of cosecant of the elevation angle with the power one-dimensional ionospheric irregularity spectral index (p) [13]. In order to simplify it more and avoid the dependence on the filter factor F(a,b), which is a complicated function of the ionospheric irregularity elongation parameters and which makes the overall ionospheric irregularity orientation dependence very complicated [13], we have considered the ionospheric radio-wave propagation environment as isotropic [9], and this turns F(a, b) = 1. Now if we plot log-log maps for S4 and Sin (E) angle, we can calculate the one-dimensional spectra index (p) from this relationship. Once we have spectral index, we can correct the scintillation for the geometry of propagation between the receiver and the transmitter using the Eq. (6):

S4\_corrected <sup>¼</sup> S4\_observed*=*csc Eð Þð Þ <sup>P</sup>þ<sup>2</sup> *<sup>=</sup>*<sup>4</sup> (6)

**Figure 1** shows the non-corrected (S4\_observed) Vs-corrected scintillation indices (S4\_corrected) observed from the GPS scintillation receiver GSV 4004b deployed at

factor and is a function of ionospheric irregularity elongation parameters F(a,b), "a" is the elongation parameter of the irregularities along the filed lines and "b" is used for elongation across the geomagnetic field lines; υ is the three-dimensional

**Ionospheric irregularity form**

2 a:a (i.e., a = b) Sheets elongated both along the magnetic field and in transverse plane coinciding with local L shell

4 a:b and a < b This combination is impossible, as ionospheric structures cannot

have their spread more in transverse direction of the geomagnetic

are the distance to the source; *ℊ* is the common geometry and propagation factor, which is also a function of elongation parameters a and b; and <*δφ*<sup>2</sup>> is the phase variance in the satellite signal after passing through the ionospheric irregularity. Following the assumption of the theory of wave propagation in random medium, it is safe to assume at the signal frequency of interest that only the phase of the signal wave gets distorted [2] as the signal passes through the ionospheric irregularity of slab thickness L, and GPS scintillation receivers can observe the time series of the phase-modulated signal on the ground. Formulas (1) and (2) can be used to simulate the amplitude and phase scintillation. Study of the power spectrum of the ionospheric data improves the estimation of the local ionospheric irregularity form and their orientation with respect to the local geomagnetic field. Several ionospheric parameters such us spectral index, turbulence strength parameter, and phase fluctuations are essential for correcting the data contaminated through the

Following Eq. (2) discussed in Rino [9], scintillation index is a function of Fresnel filter factor F(a,b) which is a function of elongation parameters "a" and "b." A detailed explanation of this function is discussed in Rino [9]; as it is beyond the scope of this book chapter therefore we are not discussing it in more details. But, for the reader's convenience, we are providing a summary table (please see **Table 1**), which summarizes the different combinations of "a" and "b" giving rise to different

Scintillation modeling provides a general scenario of the ionospheric scintillations' global morphology and occurrence during different solar activity and space weather conditions. It is always not possible to obtain ionospheric scintillation observation during some space weather events, and during such situations, a realistic ionospheric scintillation model can be used to fill the data gaps. On the other hand, data assimilation techniques also use scintillation model to assimilate scintillation observation into the scintillation model in order to improve the forecast. There are two aspects that influence the occurrence and strength of the ionospheric scintillation. First is the geometrical effect between the radio-wave-emitting satellite and the receiver which can be at any different location, e.g., at the ground;

<sup>4</sup>*<sup>π</sup>* ; ZR = ZZs/(Z + Zs) and Zs

spectral index; q0 is the inner scale constant and Z ➔*λZRSec<sup>θ</sup>*

field

geometrical errors.

**94**

**S. No. Elongation**

**Table 1.**

**parameters' axial ratio**

1 a:1 (i.e., b = 1) Field aligned rods

*Satellites Missions and Technologies for Geosciences*

3 a:b and a > b Like wings

*Form of the ionospheric irregularities.*

ionospheric irregularity shapes.

**3. Scintillation modeling needs and discussions**

the Hornsund, Svalbard (76.9718° N, 15.7844° E). The red dots show the amplitude scintillation index, whereas the black dots show the phase scintillation index. If we compare these two figures, the gap between amplitude and phase scintillation index is very less in uncorrected case (on the left). We see that the number of amplitude and phase scintillation observations is reduced in the corrected case (on the right). It is evident from **Figure 1** that after correcting the scintillation data, we come over the geometrical effect for the amplitude as well as phase scintillation. After the geometrical effect correction, we observe two main things. Firstly, there is a reduction in the numerical peak value of the scintillation indices, and secondly, we do see the significant numerical level gap between amplitude and phase scintillation index data after the geometrical correction.

Here we see the scintillation index maximizes between 15 and 20° elevation angles as well as between 70 and 85° elevation angles. At 15–20° elevation angle, the multipath effects are dominant due to which we observe high-value scintillations. On the other hand, between 70 and 85° elevation, the angle orientation of the geomagnetic field line at Hornsund is such that it forms rodlike structure along the geomagnetic field lines. At certain combination of the elevation and azimuth angle, the receiver looks along the geomagnetic field lines. Due to such ionospheric irregularity structure orientation, GPS/GNSS signal travels more distance through the ionospheric irregularity; consequently, we observe the high numerical value of the

To demonstrate the simultaneous impact of the ionospheric irregularity structure and its orientation with respect to the local geomagnetic field lines, we have simulated amplitude scintillation index for a mid-latitude regions Weihai (geographic latitude 37.53°, geographic longitude 122.05°). We have simulated a midlatitude region for simulating the scintillation index (please see **Figure 3**) using the method discussed in Priyadarshi and Wernik [13] because at the mid-latitude orientation of the geomagnetic field line is neither completely horizontal (alike the

Such slant orientation of the geomagnetic field with respect to the local Earth's surface allows the ionospheric irregularities to evolve and settle in several forms. The orientation parameters along and across the geomagnetic field lines are shown in **Figure 3a** and **b**. The top-left panel shows the rodlike structure, in which we see that for lower elevation angle (≤10°), the scintillation index is very high. As the elevation angle gradually increases, there is a reduction in the scintillation index, and the scintillation index is minimal for higher elevation angle (≥70°). It means that when the spread of the ionospheric irregularity is not significant neither along

a = 1; b=1 a = 1; b=10

a = 10; b=1 a = 10; b=10

*Simulation scintillation indices for the different combinations of the ionospheric irregularity elongation parameters: rodlike (top-left), winglike (top-right and bottom-left), and sheetlike (bottom-right).*

**Figure 3.**

**97**

scintillation indices at this elevation angle range.

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

equatorial region) nor completely vertical (as at the polar region).

**Figure 2** shows the normalized simulated scintillation index map (scintillation index divided by the scintillation index at 25° elevation angle). This map shows the scintillation index simulation for a high-latitude station in Hornsund, Svalbard.

#### **Figure 1.**

*Amplitude (in orange) and phase scintillation (in black) index observations for Hornsund, Svalbard (76.9718° N, 15.7844° E), (on left) without geometrical effect correction, (on right) with geometrical effect correction.*

#### **Figure 2**.

*Normalized simulated amplitude scintillation index for Hornsund, Svalbard, vs elevation angle of the transmitter.*

#### *Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

the Hornsund, Svalbard (76.9718° N, 15.7844° E). The red dots show the amplitude scintillation index, whereas the black dots show the phase scintillation index. If we compare these two figures, the gap between amplitude and phase scintillation index is very less in uncorrected case (on the left). We see that the number of amplitude and phase scintillation observations is reduced in the corrected case (on the right). It is evident from **Figure 1** that after correcting the scintillation data, we come over the geometrical effect for the amplitude as well as phase scintillation. After the geometrical effect correction, we observe two main things. Firstly, there is a reduction in the numerical peak value of the scintillation indices, and secondly, we do see the significant numerical level gap between amplitude and phase

**Figure 2** shows the normalized simulated scintillation index map (scintillation index divided by the scintillation index at 25° elevation angle). This map shows the scintillation index simulation for a high-latitude station in Hornsund, Svalbard.

*Amplitude (in orange) and phase scintillation (in black) index observations for Hornsund, Svalbard (76.9718° N, 15.7844° E), (on left) without geometrical effect correction, (on right) with geometrical effect*

*Normalized simulated amplitude scintillation index for Hornsund, Svalbard, vs elevation angle of the*

scintillation index data after the geometrical correction.

*Satellites Missions and Technologies for Geosciences*

**Figure 1.**

*correction.*

**Figure 2**.

*transmitter.*

**96**

Here we see the scintillation index maximizes between 15 and 20° elevation angles as well as between 70 and 85° elevation angles. At 15–20° elevation angle, the multipath effects are dominant due to which we observe high-value scintillations. On the other hand, between 70 and 85° elevation, the angle orientation of the geomagnetic field line at Hornsund is such that it forms rodlike structure along the geomagnetic field lines. At certain combination of the elevation and azimuth angle, the receiver looks along the geomagnetic field lines. Due to such ionospheric irregularity structure orientation, GPS/GNSS signal travels more distance through the ionospheric irregularity; consequently, we observe the high numerical value of the scintillation indices at this elevation angle range.

To demonstrate the simultaneous impact of the ionospheric irregularity structure and its orientation with respect to the local geomagnetic field lines, we have simulated amplitude scintillation index for a mid-latitude regions Weihai (geographic latitude 37.53°, geographic longitude 122.05°). We have simulated a midlatitude region for simulating the scintillation index (please see **Figure 3**) using the method discussed in Priyadarshi and Wernik [13] because at the mid-latitude orientation of the geomagnetic field line is neither completely horizontal (alike the equatorial region) nor completely vertical (as at the polar region).

Such slant orientation of the geomagnetic field with respect to the local Earth's surface allows the ionospheric irregularities to evolve and settle in several forms. The orientation parameters along and across the geomagnetic field lines are shown in **Figure 3a** and **b**. The top-left panel shows the rodlike structure, in which we see that for lower elevation angle (≤10°), the scintillation index is very high. As the elevation angle gradually increases, there is a reduction in the scintillation index, and the scintillation index is minimal for higher elevation angle (≥70°). It means that when the spread of the ionospheric irregularity is not significant neither along

#### **Figure 3.**

*Simulation scintillation indices for the different combinations of the ionospheric irregularity elongation parameters: rodlike (top-left), winglike (top-right and bottom-left), and sheetlike (bottom-right).*

nor across the geomagnetic field lines, then the strength of the scintillation solely depends on the elevation angles. When the spread of the ionospheric structures are more either along (bottom-left) or across (top-right) the geomagnetic field lines, then the numerical value of the scintillation index depends on the combination of the elevation angle and azimuth and on the orientation of the irregularities with respect to the local geomagnetic field lines. As we can see that even for the winglike structures, the scintillation behavior is different for the irregularity spread along and across the geomagnetic field lines. For a = 1 and b = 10 (top-right figure), it shows the decrease in the scintillation with increasing elevation angle, and it also shows scintillation maximizes near two azimuth angles nearly 90° and 270°, respectively. At these two azimuth angles, the line of sight of the observer goes close to the vertical direction of the geomagnetic field lines; ionospheric irregularity structure orientations are vertical and in the direction of the geomagnetic field line. Due to such vertical irregularity orientation, we observe the sharp enhancement in the scintillation index. The third case (a = 10 and b = 1; bottom-left) seems quite similar to the case discussed in the figure at the top-left (a = 1 and b = 1), except 300° > azimuth<50°. In the fourth case (a = 10 and b = 10; bottom-right), there are two spikes at the azimuth 90° and 270°; these spikes are due to the direction of the geomagnetic field effect as well as a gradual decrease in the scintillation with increasing elevation angle. In summary, a certain combination of azimuth and elevation angle and orientation of the geomagnetic field allow radio-wave satellite signal passing more through the ionospheric irregularity structure.

#### **3.2 Limitations as well as efficacy in modeling the scintillation, due to solar activity, local seasons, local time, and geomagnetic activity**

It is very challenging to model ionospheric scintillation during different solar activity, season, local time, and geomagnetic activity conditions. The first limitation for empirical modeling may be due to the specific data unavailability during the particular geomagnetic, solar, and local seasons/time conditions. The modeler should be very careful in making the scintillation model sensitive to the several ionospheric anomalies that appear at different geographic locations and seasons. For example, winter anomaly causes more ionospheric scintillation production during the winter months than the summer months in mid-latitude regions. 20° of the magnetic equator is the equatorial anomaly region. In the equatorial anomaly region, we observe strong ionospheric scintillation as a lot of scintillation-producing ionospheric irregularity deposit in these regions due to the fountain effect. At the equatorial electrojet regions which are at 3° of the magnetic equator, we also observe severe ionospheric scintillations. Other events such as X-rays, sudden ionospheric disturbances (SID); protons, polar cap absorption (PCA); and geomagnetic storms and lightning can also produce a significant amount of scintillationproducing ionospheric irregularities.

universal time hours (UT). As evident from **Figure 4** (between 60 and 70° MLAT), during a weak geomagnetic storm (30 > =Dst > = 50 nT) that occurred on 03 January 2014, the amplitude scintillation index shows quite similar fluctuations with the phase scintillation index (please see 16–20 MLT at 60–70° S CGMLAT; 0–6 MLT at 60–70° S CGMLAT, and 10–14 MLT at 60–70° S CGMLAT). But, overall phase scintillation values were relatively higher than the amplitude scintillation index. However, during a strong geomagnetic storm (Dst < = 100 nT) that occurred on 27 February 2014, we see that some part of the amplitude scintillation index shows resemblance with the phase scintillation index map, for instance, 0–2 MLT and 16–18 MLT at 60–70° CGMLAT, but, in the rest of the part near the dusk regions at all the CGMLATs 60–90° S CGMLAT, phase scintillation indices are much higher and high in occurrence than the amplitude scintillation index. It should be noted here that it is not compulsory that S4 and σφ have similar character of variations. In general it depends on the scale of the ionospheric turbulences. In case of kilometers size, the refractive scintillations of the phase are predominant, but amplitude scintillations are minimal. In case of hundred metersized turbulence, both refractive and diffractive mechanisms are present, and, hence S4 and σφ indices have high similarity of variations (please see [19] for

*Modeled amplitude and phase scintillation index over Antarctica during a moderate geomagnetic storm that*

Space physicist generally explains this that during such strong geomagnetic storms, large-scale ionospheric irregularity structures often enter from the cusp region, and following the convection cells, they pass through the deep inside the polar cap and exit near the magnetic midnight or dusk sector of the polar region. This explanation seems true, but, following the scintillation theory described in Wernik et al. [1], when phase scintillation index keeps on increasing but amplitude

details).

**99**

**Figure 4.**

*occurred on 03 January 2014.*

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

On the other hand, at the polar latitudes, it is widely believed that the phase scintillation index is more sensitive to the solar/geomagnetic events than the amplitude scintillation index [14–17]. Let us understand this by an illustrative example. Following the South Pole scintillation model developed by Priyadarshi et al. [18], we have produced modeled amplitude and phase scintillation maps for two geomagnetic storms, which occurred in the year 2014 (please see **Figures 4** and **5**). This South Pole empirical scintillation model uses two b-spline functions of degree 4, along with the Ap index and scintillation observation recorded over South Pole GSV4004 scintillation receiver (location, 89.99° geographic latitude; 93.77° geographic longitude; geomagnetic coordinate, 73.5°, 127.8°). Since the model uses a single GPS receiver data, therefore, MLT time lags by 4 hours and 22 minutes to the *Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

**Figure 4.**

nor across the geomagnetic field lines, then the strength of the scintillation solely depends on the elevation angles. When the spread of the ionospheric structures are more either along (bottom-left) or across (top-right) the geomagnetic field lines, then the numerical value of the scintillation index depends on the combination of the elevation angle and azimuth and on the orientation of the irregularities with respect to the local geomagnetic field lines. As we can see that even for the winglike structures, the scintillation behavior is different for the irregularity spread along and across the geomagnetic field lines. For a = 1 and b = 10 (top-right figure), it shows the decrease in the scintillation with increasing elevation angle, and it also shows scintillation maximizes near two azimuth angles nearly 90° and 270°, respectively. At these two azimuth angles, the line of sight of the observer goes close to the vertical direction of the geomagnetic field lines; ionospheric irregularity structure orientations are vertical and in the direction of the geomagnetic field line. Due to such vertical irregularity orientation, we observe the sharp enhancement in the scintillation index. The third case (a = 10 and b = 1; bottom-left) seems quite similar to the case discussed in the figure at the top-left (a = 1 and b = 1), except 300° > azimuth<50°. In the fourth case (a = 10 and b = 10; bottom-right), there are two spikes at the azimuth 90° and 270°; these spikes are due to the direction of the geomagnetic field effect as well as a gradual decrease in the scintillation with increasing elevation angle. In summary, a certain combination of azimuth and elevation angle and orientation of the geomagnetic field allow radio-wave satellite

*Satellites Missions and Technologies for Geosciences*

signal passing more through the ionospheric irregularity structure.

**activity, local seasons, local time, and geomagnetic activity**

storms and lightning can also produce a significant amount of scintillation-

On the other hand, at the polar latitudes, it is widely believed that the phase scintillation index is more sensitive to the solar/geomagnetic events than the amplitude scintillation index [14–17]. Let us understand this by an illustrative example. Following the South Pole scintillation model developed by Priyadarshi et al. [18], we have produced modeled amplitude and phase scintillation maps for two geomagnetic storms, which occurred in the year 2014 (please see **Figures 4** and **5**). This South Pole empirical scintillation model uses two b-spline functions of degree 4, along with the Ap index and scintillation observation recorded over South Pole GSV4004 scintillation receiver (location, 89.99° geographic latitude; 93.77° geographic longitude; geomagnetic coordinate, 73.5°, 127.8°). Since the model uses a single GPS receiver data, therefore, MLT time lags by 4 hours and 22 minutes to the

producing ionospheric irregularities.

**98**

**3.2 Limitations as well as efficacy in modeling the scintillation, due to solar**

It is very challenging to model ionospheric scintillation during different solar activity, season, local time, and geomagnetic activity conditions. The first limitation for empirical modeling may be due to the specific data unavailability during the particular geomagnetic, solar, and local seasons/time conditions. The modeler should be very careful in making the scintillation model sensitive to the several ionospheric anomalies that appear at different geographic locations and seasons. For example, winter anomaly causes more ionospheric scintillation production during the winter months than the summer months in mid-latitude regions. 20° of the magnetic equator is the equatorial anomaly region. In the equatorial anomaly region, we observe strong ionospheric scintillation as a lot of scintillation-producing ionospheric irregularity deposit in these regions due to the fountain effect. At the equatorial electrojet regions which are at 3° of the magnetic equator, we also observe severe ionospheric scintillations. Other events such as X-rays, sudden ionospheric disturbances (SID); protons, polar cap absorption (PCA); and geomagnetic

*Modeled amplitude and phase scintillation index over Antarctica during a moderate geomagnetic storm that occurred on 03 January 2014.*

universal time hours (UT). As evident from **Figure 4** (between 60 and 70° MLAT), during a weak geomagnetic storm (30 > =Dst > = 50 nT) that occurred on 03 January 2014, the amplitude scintillation index shows quite similar fluctuations with the phase scintillation index (please see 16–20 MLT at 60–70° S CGMLAT; 0–6 MLT at 60–70° S CGMLAT, and 10–14 MLT at 60–70° S CGMLAT). But, overall phase scintillation values were relatively higher than the amplitude scintillation index. However, during a strong geomagnetic storm (Dst < = 100 nT) that occurred on 27 February 2014, we see that some part of the amplitude scintillation index shows resemblance with the phase scintillation index map, for instance, 0–2 MLT and 16–18 MLT at 60–70° CGMLAT, but, in the rest of the part near the dusk regions at all the CGMLATs 60–90° S CGMLAT, phase scintillation indices are much higher and high in occurrence than the amplitude scintillation index. It should be noted here that it is not compulsory that S4 and σφ have similar character of variations. In general it depends on the scale of the ionospheric turbulences. In case of kilometers size, the refractive scintillations of the phase are predominant, but amplitude scintillations are minimal. In case of hundred metersized turbulence, both refractive and diffractive mechanisms are present, and, hence S4 and σφ indices have high similarity of variations (please see [19] for details).

Space physicist generally explains this that during such strong geomagnetic storms, large-scale ionospheric irregularity structures often enter from the cusp region, and following the convection cells, they pass through the deep inside the polar cap and exit near the magnetic midnight or dusk sector of the polar region. This explanation seems true, but, following the scintillation theory described in Wernik et al. [1], when phase scintillation index keeps on increasing but amplitude

ionospheric scintillation. If taken care well, the model limitations caused due to geometrical effects such as multipath, elevation angle dependence, and form of ionospheric irregularities can be reduced, and the modeled results would provide an error-free estimation of the scintillation indices. Scintillation observations are one of the scintillation model input. By studying the log-log variation of the scintillation index, we can derive the scintillation index correction parameters as discussed in Section 2 [13]. These scintillation correction parameters are used to correct the scintillation model input data. We have demonstrated the geometric corrections between the satellite and receivers (see **Figure 1**) and its importance to use corrected scintillation indices as model input for modeling the ionospheric scintillation indices. Ionospheric scintillation highly depends on the orientation as well as the spread of the ionospheric irregularities with respect to the local geomagnetic field lines. In order to get the real and justified model's output, we must use the input scintillation data in our model for all available, local seasons, local time, and solar/geomagnetic activity duration. At certain elevation angles, scintillation indices show unusual enhancement (please see **Figure 2**); it is because the signal travels long distances through the ionospheric irregularity structures at the lower elevation angle, and at certain elevation angle, the receiver look along the vertical orientation of the ionospheric irregularities with respect to the local geomagnetic field line. The orientation of the ionospheric irregularities structures influences the ionospheric scintillation intensity. We have compared the four types of combination of ionospheric irregularity structures using four different combinations of elongation axial ratios "a" and "b" (please see **Figure 3** and its related discussion). **Figure 3** demonstrated that at a few combinations of azimuth and elevation angles, the orientation of the geomagnetic field allows radio-wave satellite signal passing more through the ionospheric irregularity structure. Apparently, scintillation intensity enhances at these locations. Through two modeled South Pole scintillation maps during the moderate and strong geomagnetic storms (case studies related to **Figures 4** and **5**), we have demonstrated that the adoption of the scintillation model provides its high case sensitive to the latitudinal anomalies. For example, winter anomaly which occurs at the mid-latitude ionosphere that causes more scintillation occurrence in winter months as compared to the summer months enhanced scintillation 20° to either side of the geomagnetic equator and EEJ effect which occurs 3° on either side of the geomagnetic equator. For high-latitude scintillation modeling, it is essential to consider the ionospheric ionization difference during the sunlit months and dark months. The importance of optimizing the numerical scale of phase and amplitude scintillation indices at the polar latitudes is also discussed. In summary, the presented book chapter discusses all the factors that significantly influence the ionospheric scintillation and possible methods to minimize the estimation errors that caused them. Physics-based models are good to produce the global morphology of the ionospheric scintillation, but, they often fail to produce the exact scintillation index during active solar events. Therefore, empirical models are better as they use the physics-based model as background, but they keep on using the real observations as the model input. These joint efforts work in the derivation and development of a decent ionospheric scintillation model, which can produce equivalent scintillation indices for all the geographic latitudes, local weather, local time, and solar/

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

geomagnetic activity conditions.

The scintillation data used in this book chapter has been thankfully obtained

from the Polish Polar Station Hornsund, Svalbard. We also thank the Space Research Centre, Polish Academy of Sciences (SRC-PAS) for requesting this data.

**Acknowledgements**

**101**

**Figure 5.**

*Modeled amplitude and phase scintillation index over Antarctica during a strong geomagnetic storm that occurred on 27 February 2014.*

scintillation seizes to increase, then at this instance, the ionospheric irregularity plasma waves are not more coherent, and these fluctuations cannot be considered as real scintillation due to lack of interference between two noncoherent plasma waves at high-phase fluctuations [1]. For this situation the modeler must optimize the numerical scales of the observed scintillation indices such that the amplitude and phase scintillation indices show similar fluctuation to each other. Priyadarshi et al. [20] used an optimized numerical scale for the amplitude and phase scintillation indices observed during a geomagnetic storm that occurred on 27 February 2014. Ionospheric scintillation index optimization is a way in which by using different amplitude and phase scintillation variation scale it helps us see the same trend variation in the amplitude/phase scintillation indices. In the event discussed in Priyadarshi et al. [20], phase scintillation index variations were optimized between 0.05 and 0.5, and amplitude scintillation index variations were optimized between 0.05 and 0.2. The dayside cusp region amplitude and phase scintillation indices gave similar information at different numerical scales. This study also demonstrated that the amplitude scintillation index is also a useful scintillation index if the proper numerical scale is chosen. Large-scale ionospheric irregularities such as storm-enhanced density (SED) and tong of ionization (TOI) were not found necessarily producing ionospheric scintillation [20].

#### **4. Summary**

In general it is more typical when the carrier-phase loss of lock happens due to the sharp signal-to-noise ratio fading (please look [21, 22] for more details). In this book chapter, we have discussed some peculiar modeling tricks and tips of

#### *Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

ionospheric scintillation. If taken care well, the model limitations caused due to geometrical effects such as multipath, elevation angle dependence, and form of ionospheric irregularities can be reduced, and the modeled results would provide an error-free estimation of the scintillation indices. Scintillation observations are one of the scintillation model input. By studying the log-log variation of the scintillation index, we can derive the scintillation index correction parameters as discussed in Section 2 [13]. These scintillation correction parameters are used to correct the scintillation model input data. We have demonstrated the geometric corrections between the satellite and receivers (see **Figure 1**) and its importance to use corrected scintillation indices as model input for modeling the ionospheric scintillation indices. Ionospheric scintillation highly depends on the orientation as well as the spread of the ionospheric irregularities with respect to the local geomagnetic field lines. In order to get the real and justified model's output, we must use the input scintillation data in our model for all available, local seasons, local time, and solar/geomagnetic activity duration. At certain elevation angles, scintillation indices show unusual enhancement (please see **Figure 2**); it is because the signal travels long distances through the ionospheric irregularity structures at the lower elevation angle, and at certain elevation angle, the receiver look along the vertical orientation of the ionospheric irregularities with respect to the local geomagnetic field line. The orientation of the ionospheric irregularities structures influences the ionospheric scintillation intensity. We have compared the four types of combination of ionospheric irregularity structures using four different combinations of elongation axial ratios "a" and "b" (please see **Figure 3** and its related discussion). **Figure 3** demonstrated that at a few combinations of azimuth and elevation angles, the orientation of the geomagnetic field allows radio-wave satellite signal passing more through the ionospheric irregularity structure. Apparently, scintillation intensity enhances at these locations. Through two modeled South Pole scintillation maps during the moderate and strong geomagnetic storms (case studies related to **Figures 4** and **5**), we have demonstrated that the adoption of the scintillation model provides its high case sensitive to the latitudinal anomalies. For example, winter anomaly which occurs at the mid-latitude ionosphere that causes more scintillation occurrence in winter months as compared to the summer months enhanced scintillation 20° to either side of the geomagnetic equator and EEJ effect which occurs 3° on either side of the geomagnetic equator. For high-latitude scintillation modeling, it is essential to consider the ionospheric ionization difference during the sunlit months and dark months. The importance of optimizing the numerical scale of phase and amplitude scintillation indices at the polar latitudes is also discussed. In summary, the presented book chapter discusses all the factors that significantly influence the ionospheric scintillation and possible methods to minimize the estimation errors that caused them. Physics-based models are good to produce the global morphology of the ionospheric scintillation, but, they often fail to produce the exact scintillation index during active solar events. Therefore, empirical models are better as they use the physics-based model as background, but they keep on using the real observations as the model input. These joint efforts work in the derivation and development of a decent ionospheric scintillation model, which can produce equivalent scintillation indices for all the geographic latitudes, local weather, local time, and solar/ geomagnetic activity conditions.

#### **Acknowledgements**

The scintillation data used in this book chapter has been thankfully obtained from the Polish Polar Station Hornsund, Svalbard. We also thank the Space Research Centre, Polish Academy of Sciences (SRC-PAS) for requesting this data.

scintillation seizes to increase, then at this instance, the ionospheric irregularity plasma waves are not more coherent, and these fluctuations cannot be considered as real scintillation due to lack of interference between two noncoherent plasma waves at high-phase fluctuations [1]. For this situation the modeler must optimize the numerical scales of the observed scintillation indices such that the amplitude and phase scintillation indices show similar fluctuation to each other. Priyadarshi et al. [20] used an optimized numerical scale for the amplitude and phase scintillation indices observed during a geomagnetic storm that occurred on 27 February 2014. Ionospheric scintillation index optimization is a way in which by using different amplitude and phase scintillation variation scale it helps us see the same trend variation in the amplitude/phase scintillation indices. In the event discussed in Priyadarshi et al. [20], phase scintillation index variations were optimized between 0.05 and 0.5, and amplitude scintillation index variations were optimized between 0.05 and 0.2. The dayside cusp region amplitude and phase scintillation indices gave similar information at different numerical scales. This study also demonstrated that the amplitude scintillation index is also a useful scintillation index if the proper numerical scale is chosen. Large-scale ionospheric irregularities such as storm-enhanced density (SED) and tong of ionization (TOI) were not found

*Modeled amplitude and phase scintillation index over Antarctica during a strong geomagnetic storm that*

In general it is more typical when the carrier-phase loss of lock happens due to the sharp signal-to-noise ratio fading (please look [21, 22] for more details). In this

book chapter, we have discussed some peculiar modeling tricks and tips of

necessarily producing ionospheric scintillation [20].

**4. Summary**

**100**

**Figure 5.**

*occurred on 27 February 2014.*

*Satellites Missions and Technologies for Geosciences*

### **Conflict of interest**

S. Priyadarshi is the principal as well as the corresponding author of this book chapter. The text, as well as figures presented in this book chapter, has not been published anywhere else before.

**References**

[1] Wernik AW, Alfonsi L, Materassi M. Ionospheric irregularities, scintillation and its effect on systems. Acta Geologica

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

> Weak scatter. Radio Science. 1979; **14**(6):1135-1145. DOI: 10.1029/RS014

[10] Ma G, Maruyama T. A super bubble detected by dense GPS network at east Asian longitudes. Geophysical Research

[11] Demyanov VV, Yasyukevich Yu V, Ishin AB, Astafyeva EI. Effects of ionosphere super-bubble on the GPS positioning performance depending on the orientation relative to geomagnetic field. GPS Solutions. 2012;**16**:181-189. DOI: 10.1007/s10291-011-0217-9

[12] Booker HG, Ratcliffe JA, Shinn DH. Diffraction from an irregular screen with applications to ionospheric

problems. Philosophical Transactions of the Royal Society. 1950;**242**(856): 579-607. DOI: 10.1098/rsta.1950.0011

Variation of the ionospheric scintillation

Franceschi G, Romano V, Aquino MHO,

ionospheric scintillations over high and mid latitude European regions. Annales de Geophysique. 2009;**27**(9):3429-3437

[15] Li GZ, Ning BQ, Ren ZP, Hu LH.

scintillation and irregularities over polar

[13] Priyadarshi S, Wernik AW.

index with elevation angle of the transmitter. 2013:**61**(5):1279-1288. DOI:

10.2478/s11600-013-0123-3

[14] Spogli L, Alfonsi L, De

Dodson A. Climatology of GPS

Statistics of GPS ionospheric

regions at solar minimum. GPS Solutions. 2010;**14**(4):331-341

[16] Prikryl P, Jayachandran PT, Mushini SC, Pokhotelov D, Mac-Dougall JW, Donovan E, et al. GPS TEC, scintillation and cycle slips observed at high latitudes during solar minimum. Annales de Geophysique. 2010;**28**(6):

1307-1316

i006p01135

Letters. 2006;**33**:L21103

[2] Wernik AW, Alfonsi L, Materassi M. Scintillation modeling using in situ data. Radio Science. 2007;**42**:RS1002. DOI:

Polonica. 2004;**52**(2):237-249

[3] Singleton DG. Dependence of satellite scintillations on zenith angle and azimuth. Journal of Atmospheric and Terrestrial Physics. 1970;**32**(5): 789-803. DOI: 10.1016/0021-9169 (70)

[4] Briggs BH, Parkin IA. On the variation of radio star and satellite scintillations with zenith angle. Journal of Atmospheric and Terrestrial Physics. 1963;**25**(6):339-366. DOI: 10.1016/

[6] Current IGS Site Guidelines.

us/articles/202011433-Current-

[7] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/

[8] McCaffrey AM, Jayachandran PT. Spectral characteristics of auroral region scintillation using 100 Hz sampling. GPS Solutions. 2017;**21**:1883-1894. DOI:

[9] Rino CL. A power law phase screen model for ionospheric scintillation: 1.

10.1007/s10291-017-0664-z

IGS-Site-Guidelines

s00190-018-1172-9

**103**

[5] Forte B, Radicella SM. Comparison of ionospheric scintillation models with experimental data for satellite navigation applications. Annales de Geophysique. 2005;**48**(3):505-514

Available from: https://kb.igs.org/hc/en-

0021-9169(63)90150-8

10.1029/2006RS003512

90029-2

### **Author details**

Shishir Priyadarshi Department of Climatology and Atmospheric Protection, University of Wroclaw, Poland

\*Address all correspondence to: shishir.priyadarshi@uwr.edu.pl

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Ionospheric Scintillation Modeling Needs and Tricks DOI: http://dx.doi.org/10.5772/intechopen.88313*

#### **References**

**Conflict of interest**

**Author details**

Shishir Priyadarshi

Poland

**102**

published anywhere else before.

*Satellites Missions and Technologies for Geosciences*

S. Priyadarshi is the principal as well as the corresponding author of this book chapter. The text, as well as figures presented in this book chapter, has not been

Department of Climatology and Atmospheric Protection, University of Wroclaw,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: shishir.priyadarshi@uwr.edu.pl

provided the original work is properly cited.

[1] Wernik AW, Alfonsi L, Materassi M. Ionospheric irregularities, scintillation and its effect on systems. Acta Geologica Polonica. 2004;**52**(2):237-249

[2] Wernik AW, Alfonsi L, Materassi M. Scintillation modeling using in situ data. Radio Science. 2007;**42**:RS1002. DOI: 10.1029/2006RS003512

[3] Singleton DG. Dependence of satellite scintillations on zenith angle and azimuth. Journal of Atmospheric and Terrestrial Physics. 1970;**32**(5): 789-803. DOI: 10.1016/0021-9169 (70) 90029-2

[4] Briggs BH, Parkin IA. On the variation of radio star and satellite scintillations with zenith angle. Journal of Atmospheric and Terrestrial Physics. 1963;**25**(6):339-366. DOI: 10.1016/ 0021-9169(63)90150-8

[5] Forte B, Radicella SM. Comparison of ionospheric scintillation models with experimental data for satellite navigation applications. Annales de Geophysique. 2005;**48**(3):505-514

[6] Current IGS Site Guidelines. Available from: https://kb.igs.org/hc/enus/articles/202011433-Current-IGS-Site-Guidelines

[7] Padma B, Kai B. Performance analysis of dual-frequency receiver using combinations of GPS L1, L5, and L2 civil signals. Journal of Geodesy. 2019;**93**:437-447. DOI: 10.1007/ s00190-018-1172-9

[8] McCaffrey AM, Jayachandran PT. Spectral characteristics of auroral region scintillation using 100 Hz sampling. GPS Solutions. 2017;**21**:1883-1894. DOI: 10.1007/s10291-017-0664-z

[9] Rino CL. A power law phase screen model for ionospheric scintillation: 1.

Weak scatter. Radio Science. 1979; **14**(6):1135-1145. DOI: 10.1029/RS014 i006p01135

[10] Ma G, Maruyama T. A super bubble detected by dense GPS network at east Asian longitudes. Geophysical Research Letters. 2006;**33**:L21103

[11] Demyanov VV, Yasyukevich Yu V, Ishin AB, Astafyeva EI. Effects of ionosphere super-bubble on the GPS positioning performance depending on the orientation relative to geomagnetic field. GPS Solutions. 2012;**16**:181-189. DOI: 10.1007/s10291-011-0217-9

[12] Booker HG, Ratcliffe JA, Shinn DH. Diffraction from an irregular screen with applications to ionospheric problems. Philosophical Transactions of the Royal Society. 1950;**242**(856): 579-607. DOI: 10.1098/rsta.1950.0011

[13] Priyadarshi S, Wernik AW. Variation of the ionospheric scintillation index with elevation angle of the transmitter. 2013:**61**(5):1279-1288. DOI: 10.2478/s11600-013-0123-3

[14] Spogli L, Alfonsi L, De Franceschi G, Romano V, Aquino MHO, Dodson A. Climatology of GPS ionospheric scintillations over high and mid latitude European regions. Annales de Geophysique. 2009;**27**(9):3429-3437

[15] Li GZ, Ning BQ, Ren ZP, Hu LH. Statistics of GPS ionospheric scintillation and irregularities over polar regions at solar minimum. GPS Solutions. 2010;**14**(4):331-341

[16] Prikryl P, Jayachandran PT, Mushini SC, Pokhotelov D, Mac-Dougall JW, Donovan E, et al. GPS TEC, scintillation and cycle slips observed at high latitudes during solar minimum. Annales de Geophysique. 2010;**28**(6): 1307-1316

[17] Moen J, Oksavik K, Alfonsi L, Daabakk Y, Romano V, Spogli L. Space weather challenges of the polar cap ionosphere. Journal of Space Weather and Space Climate. 2013;**3**: A02. DOI: 10.1051/swsc/2013025

[18] Priyadarshi S, Zhang QH, Ma YZ, Wang Y, Xing ZY. Observations and modeling of ionospheric scintillations at South Pole during six X-class solar flares in 2013. Journal of Geophysical Research: Space Physics. 2016;**121**: 5737-5751. DOI: 10.1002/2016JA022833

[19] Bhattacharrya A et al. Nighttime equatorial ionosphere: GPS scintillations and differential carrier phase fluctuations. Radio Science. 2000;**35**(1): 209-224

[20] Priyadarshi S, Zhang QH, Ma YZ. Antarctica SED/TOI associated ionospheric scintillation during 27 February 2014 geomagnetic storm. Astrophysics and Space Science. 2018; **363**:262. DOI: 10.1007/s10509-018- 3484-x

[21] Tiwari R, Strangeways HJ. Regionally based alarm index to mitigate ionospheric scintillation effects for GNSS receivers. Space Weather. 2015;**13**:72-85. DOI: 10.1002/ 2014SW001115

[22] Kinter PM, Ledvina BM, de Paula ER. GPS and ionosphere scintillations. Space Weather. 2007; **5**:S09003. DOI: 10.1029/ 2006SW000260

**105**

Section 3

Satellites Missions and

Technologies

### Section 3
