**2.1. Key figures of UWB-sensors**

The UWB sensor configuration may be determined by demands which are guided by two different and partially conflicting aspects. On the one hand, these are the UWB radiation rules, and on the other one, we have to respect the physical constraints of the sensing problem. The radiation rules, which are not unique within different regions of the world, mainly limit spectral power emission, restrict the operation frequency band and require sounding signals of large instantaneous bandwidth. Seen from a physical point of view, we need an adequate operational frequency band which provides reasonable interaction between the sounding signal and the object of interest. This may lead to conflicting situations with the radiation rule for sensing tasks requiring wave penetration like throughwall radar or medical imaging. Thus, one has to search a proper compromise in the case of frequency mask violation. Though UWB sensors are banned from long-range applications due to low-radiation power, they promote biological and medical sensing since the target exposition is harmless. Furthermore, the interaction between sounding wave and target is based on linear phenomena. Hence, the sounding bandwidth may be provided instantaneously (complying with FCC or ECC radiation rules) or sequentially (violating these radiation rules) without affecting the measurement results as long as the scenario under test behaves stationary during the measurement. This paper is focused on techniques for information capture by exploiting electromagnetic interactions. Hence, we do not exclude sensor principles or frequency bands violating UWB radiation rules from our further discussions.

*Spectral band and related parameters*: As frequency diversity is a key issue of unambiguous information gathering by electric sensors, the widths and the occupation density of the spectral sounding band is of major interest. For the sake of brevity, we will deal here only with baseband signals (see [2] for deeper discussions) which we characterize by their twosided bandwidth *B* that can be linked to typical time domain parameters:

$$\underline{B} \approx \begin{cases} t\_w^{-1} & \text{for pulse shaped signal} \\ \tau\_{cob}^{-1} & \text{for CW signal} \end{cases} \tag{1}$$

Here, *wt* represents the width of a pulse, and *coh* is the coherence time of a random or pseudo-random signal (i.e. the width of the auto-correlation function). The occupation density of the frequency band is given by the line spacing *f* which is either determined by the repetition rate 0 *f* of a periodic sounding signal ( *Pt* - period duration) or via the Fourier Transform by the observation interval *T* of non-periodic signals:

370 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

Finally, some aspects of monolithically integrated UWB-sensors are discussed.

**2. Properties and basic concepts of UWB-sensors** 

**2.1. Key figures of UWB-sensors** 

further discussions.

sided bandwidth *B*

noise codes.

integration regardless of the sensor principle. The HaLoS-project addresses this topic by investigating general purpose UWB sub-modules like amplifiers, ADCs, fast processing units etc. as well as an integration-friendly sensor concept based on ultra-wideband pseudo-

The chapter is organized as follows. First, the most important performance figures of UWB sensors are introduced. Second, we give an overview of various UWB-sensor principles recently in use and explain the UWB pseudo-noise concept. Then, we address some specific topics like wideband receiver circuits, transmitter circuits and high-speed data capture.

The UWB sensor configuration may be determined by demands which are guided by two different and partially conflicting aspects. On the one hand, these are the UWB radiation rules, and on the other one, we have to respect the physical constraints of the sensing problem. The radiation rules, which are not unique within different regions of the world, mainly limit spectral power emission, restrict the operation frequency band and require sounding signals of large instantaneous bandwidth. Seen from a physical point of view, we need an adequate operational frequency band which provides reasonable interaction between the sounding signal and the object of interest. This may lead to conflicting situations with the radiation rule for sensing tasks requiring wave penetration like throughwall radar or medical imaging. Thus, one has to search a proper compromise in the case of frequency mask violation. Though UWB sensors are banned from long-range applications due to low-radiation power, they promote biological and medical sensing since the target exposition is harmless. Furthermore, the interaction between sounding wave and target is based on linear phenomena. Hence, the sounding bandwidth may be provided instantaneously (complying with FCC or ECC radiation rules) or sequentially (violating these radiation rules) without affecting the measurement results as long as the scenario under test behaves stationary during the measurement. This paper is focused on techniques for information capture by exploiting electromagnetic interactions. Hence, we do not exclude sensor principles or frequency bands violating UWB radiation rules from our

*Spectral band and related parameters*: As frequency diversity is a key issue of unambiguous information gathering by electric sensors, the widths and the occupation density of the spectral sounding band is of major interest. For the sake of brevity, we will deal here only with baseband signals (see [2] for deeper discussions) which we characterize by their two-

that can be linked to typical time domain parameters:

 for pulse shaped signal for CW signal

(1)

1 1

*w coh*

 

*t B*

$$
\Delta f = \begin{cases} f\_0 = t\_p^{-1} & \text{periodic signal} \\ T^{-1} & \text{non-periodic signal} \end{cases} \tag{2}
$$

As non-periodic signals are quite unusual in UWB sensing, we will avoid discussing them. The line spacing *f* gives the frequency resolution of the sensor or it determines the maximum observable length <sup>1</sup> *T f <sup>W</sup>* of the impulse response *gt* of a scenario under test. If *g t* does not settle down within *w P T t* , we have to anticipate time aliasing.

In the case of UWB radar sensing, we can convert (1) and (2) into corresponding spatial parameters. One of them assigns the range resolution *<sup>r</sup>* , i.e. the capability of the radar to separate two close point targets of identical reflectivity. We will refer to the usual relation ( *c* - wave velocity):

$$\mathcal{S}\_r \approx \frac{c}{2\text{ }\underline{B}} \approx \begin{cases} \frac{1}{2} \tau\_{\text{coh}} \, c & \text{for time stretched signal} \\ \frac{1}{2} t\_w \, c & \text{for pulse shaped signal} \end{cases} \tag{3}$$

even if it should be considered with care. The relation originates from narrowband radar whose sounding signal suffers not from signal deformation neither by reflection at small bodies nor by antenna transmission. In contrast to that, a UWB signal bouncing a point scatterer will sustain a twofold differentiation and further deformations due to the antennas. The unambiguous rage *ua r* of the UWB radar relates to the signal repetition by:

$$r\_{uu} = \frac{1}{2}t\_P \text{ c}\tag{4}$$

*Recording time*: UWB sensors provide, depending on their principle of work, either the impulse response function (IRF) or the frequency response function (FRF) of the scenario under test. The time needed to collect all data for one IRF or FRF (including synchronous averaging of repetitive measurements) we call recording time *RT* . Non-stationary test scenarios limit the recording time either to

$$T\_R \ B\_{\text{SC}} \le \frac{1}{2} \tag{5}$$

*SC B* - physical (single-sided) bandwidth of the scenario variation

### 372 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

or to

$$T\_R \stackrel{\mathcal{B}}{=} \frac{c}{2\left|v\right|}\tag{6}$$

HaLoS – Integrated RF-Hardware Components for Ultra-Wideband Localization and Sensing 373

 *Clutter-free dynamic range Dcl* : It refers to the level difference between receiving signal and the strongest internal clutter peak. *Dcl* determines the sensitivity to detect weak targets in the presence of a strong one, and also the strength of artefacts in radar

 *Optimum dynamic range Dopt* : Internal clutter caused by linear effects can be removed by sensor calibration as used in network analyzer measurements. A perfect calibration supposed, the erroneous signals are curtailed now by the noise floor and non-linear distortions. Hence, we get optimum conditions for a large dynamic range at the

 *Maximum dynamic range Dmax* : It is defined by the difference between 1 dB compression point and noise level. Its value gives a hint on the sensitivity to detect moving targets of weak reflectivity. In many cases, the strongest backscatter signals are caused by static objects. As long as the UWB sensor is not moved, these signals and their clutter contributions are stationary so that they may be simply removed by high-pass filtering in observation time. Hence, the detectability of moving targets is only limited by the noise level. The maximum dynamic range can be roughly estimated using the following

> 1dB max 2 2 0 0

*k T CF F R CF*

*r R r R TV TB <sup>D</sup>*


number of bits (before correlation).

transmission path.

analog and digital receiver components.

2 2

(7)


2 32 *ENOB*

 - receiver efficiency; *RT* - recording time; *V*1dB - input voltage at 1 dB compression point (before correlation); *k* - Boltzmann constant; 0 *T* - temperature; *CF* - crest factor; *F*

The left part of eq. (7) applies performance parameters of analog receivers while the right part deals with the global effective number of bits merging the performance of

 *System performance Dsys* : It relates the transmitter level to the noise level. Hence, it is given by the maximum dynamic range and the attenuation of the strongest

*Time and frequency errors*: While above mentioned device characteristics refer to ordinate quantities of a signal representation, the following features quantify the quality of the abscissa representation, i.e. the time or frequency axis. Related to this, we can observe systematic deviations like non-linear frequency or time axis representations resulting in non-equidistant sampling and distortions of frequency-time conversions. Random errors of the time or frequency axis representation, we call jitter or phase noise in the case of short time variation and drift for long term variations. Jitter (respectively phase noise) causes signal-dependent noise which is elevated at signal edges and disappears at flat signal parts.

 

interception of noise and third-order distortion lines.

images.

relation [2]:

*r* 

### *v* - radial speed of a target

Equation (5) simply indicates the Nyquist theorem telling us that the refresh rate of the measurement <sup>1</sup> *R TR* must be twice the bandwidth of the process to be observed. Relation (6) refers to the Doppler-effect. It is evoked from moving targets causing an expansion or compression of the scattered signal. If such signals are accumulated (by correlation or/and synchronous averaging) over a too long duration, they de-correlate resulting in an amplitude degradation of the receiving signal and finally in the loss of the target. Equation (6) should not be confused with Doppler ambiguity which is not relevant for UWB sensing.

**Figure 1.** Stylized impulse response (left) and level diagram (right) of an UWB sensor

*Dynamic range*: Another group of important features relates to the sensitivity of weak signal detection. For illustration, we consider Fig. 1. The illustration on the left-hand side symbolizes the response of a single target. The red line represents the transmitter pulse (or also the auto-correlation function of a wideband CW-signal), and the black line is the target return which should be the only signal visible on the receiver screen. Obviously, we may detect many signal components hampering the detection of weak targets if there are some. These perturbing signals are random noise (electronic and quantization noise) and device internal clutter. It depends on the receiving signal and may be caused by the linear (internal mismatch, cross-coupling, frequency-dependent transmission behavior of electronic compounds) and non-linear effects (e.g. device saturation). Fig. 1 (right) depicts typical dependences of the perturbations from the level of the receiving signal. Based on this, we can derive various dynamic ranges:

 *Clutter-free dynamic range Dcl* : It refers to the level difference between receiving signal and the strongest internal clutter peak. *Dcl* determines the sensitivity to detect weak targets in the presence of a strong one, and also the strength of artefacts in radar images.

372 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

**Figure 1.** Stylized impulse response (left) and level diagram (right) of an UWB sensor

*Dynamic range*: Another group of important features relates to the sensitivity of weak signal detection. For illustration, we consider Fig. 1. The illustration on the left-hand side symbolizes the response of a single target. The red line represents the transmitter pulse (or also the auto-correlation function of a wideband CW-signal), and the black line is the target return which should be the only signal visible on the receiver screen. Obviously, we may detect many signal components hampering the detection of weak targets if there are some. These perturbing signals are random noise (electronic and quantization noise) and device internal clutter. It depends on the receiving signal and may be caused by the linear (internal mismatch, cross-coupling, frequency-dependent transmission behavior of electronic compounds) and non-linear effects (e.g. device saturation). Fig. 1 (right) depicts typical dependences of the perturbations from the level of the receiving signal. Based on this, we

2 *<sup>R</sup> <sup>c</sup> T B*

Equation (5) simply indicates the Nyquist theorem telling us that the refresh rate of the

(6) refers to the Doppler-effect. It is evoked from moving targets causing an expansion or compression of the scattered signal. If such signals are accumulated (by correlation or/and synchronous averaging) over a too long duration, they de-correlate resulting in an amplitude degradation of the receiving signal and finally in the loss of the target. Equation (6) should not be confused with Doppler ambiguity which is not relevant for UWB sensing.

*v*

must be twice the bandwidth of the process to be observed. Relation

(6)

or to

*v* - radial speed of a target

can derive various dynamic ranges:

measurement <sup>1</sup> *R TR*


$$D\_{\text{max}} \approx \frac{2\,\eta\_r\,\,T\_R\,\,V\_{1\text{dB}}^2}{k\,\,T\_0\,\,\text{CF}^2\,\,F\,\,R\_0} = \frac{3\,\,\eta\_r\,\,T\_R\,\,\text{B}\,\,\text{2}}{\,\,\text{CF}^2} \tag{7}$$

*r* - receiver efficiency; *RT* - recording time; *V*1dB - input voltage at 1 dB compression point (before correlation); *k* - Boltzmann constant; 0 *T* - temperature; *CF* - crest factor; *F* - noise factor; *R*<sup>0</sup> - receiver input impedance; *B* - receiver bandwidth; *ENOB* - effective number of bits (before correlation).

The left part of eq. (7) applies performance parameters of analog receivers while the right part deals with the global effective number of bits merging the performance of analog and digital receiver components.

 *System performance Dsys* : It relates the transmitter level to the noise level. Hence, it is given by the maximum dynamic range and the attenuation of the strongest transmission path.

*Time and frequency errors*: While above mentioned device characteristics refer to ordinate quantities of a signal representation, the following features quantify the quality of the abscissa representation, i.e. the time or frequency axis. Related to this, we can observe systematic deviations like non-linear frequency or time axis representations resulting in non-equidistant sampling and distortions of frequency-time conversions. Random errors of the time or frequency axis representation, we call jitter or phase noise in the case of short time variation and drift for long term variations. Jitter (respectively phase noise) causes signal-dependent noise which is elevated at signal edges and disappears at flat signal parts.

### 374 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

Jitter limits the performance of super resolution techniques and reduces the sensor sensitivity to detect weak scattering targets in the vicinity of strong reflectors.

*Efficiency*: The term efficiency can be seen under different aspects. We will consider three of them here.

*Receiver efficiency <sup>r</sup>* (see also (7)): The receiver efficiency describes the capability of the receiver to exploit the incident signal energy. As the receiving signals are usually quite weak due to the restrictions of transmission power, one has to attach great importance to the receiver efficiency. It is determined by losses in the receiver front end, e.g. the insertion loss of filters or conversion loss of mixers or sampling gates. However, dead times for energy accumulation due to filter settling, incomplete data capture by reason of sub-sampling or incomplete exploitation of captured date due to serial instead of parallel data processing are much more important. Thus, the efficiency of recent UWB receivers is often reduced to values below 1 ‰ or even less which provides some potential for further improvements.

*Figure of Merit FoM* : In general terms, the Figure of Merit expresses the expense of energy which is required to achieve a certain effect. Two examples shall illustrate the approach. The first one deals with a Nyquist analog-to-digital converter which is aimed to digitize data with a certain rate *<sup>s</sup> f* . An obvious definition of the Figure of Merit can be:

$$FoM\_{fhsADC} = \frac{P}{2^{ENOB}} \left[ \text{J/convension} \right] \tag{8}$$

HaLoS – Integrated RF-Hardware Components for Ultra-Wideband Localization and Sensing 375

*Data throughput*: UWB sensors provide lots of data particularly if they are assigned for MIMO-systems and high measurement rate. In order to conserve energy, memory space and data transmission capacity, the sensors should not provide unnecessary data. We have six

The length of the measured impulse response should not be much longer than the

Synchronous averaging (if appropriate) should be performed immediately after data

A short word length of digitized data should be kept by avoiding high crest factor

Stationary data should be removed by feedback sampling or digital filtering

Sparse or compressive sampling [3] should be performed. However, this point will not

Without going into detail, we would like to mention at least some further aspects that influence the performance of sensor operation, too. They concern interference issues like robustness against jamming and low probability of intercept (LPIR- low probability of

The performance figures summarized above are the basis for deciding on a certain sensor configuration for a specific application. In what follows, the most popular UWB sensor principles will be tabulated and assessed with respect to the introduced performance

We divide the UWB sensor principles into two groups. While the sensors of the first group generate sounding signals of large instantaneous bandwidth, the devices belonging to the second group deal with narrowband signals swept over a large bandwidth. A thorough analysis of the different sensor concepts of both groups including a reference list can be found in [2]. Here, we will only give a short summary to get an impression of the most common sub-components of UWB sensors and to understand the advantages and

There are several UWB approaches known exploiting signals of large instantaneous bandwidth. Usually, they are denoted according the sounding signal applied by the sensor.

immediately after data capture (see also chapter 3.3.4 in [2]), and

be considered here as it would go beyond the scope of this chapter.

basic options to reduce the data throughput:

settling time of the scenario under test.

capture.

signals.

intercept radar).

**2.2. Principles of UWB-sensors** 

disadvantages of the various principles.

*2.2.1. Sensors of large instantaneous bandwidth* 

Typical representatives of this signal class are:

very wideband pseudo-noise codes

sub-nanosecond pulses

figures.

The data should be captured close to the Nyquist rate.

*P* - power dissipation of the ADC; *ENOB* - effective number of bits of the ADC; *s f* - sampling rate

Typical FoM-values for high speed ADCs are to be found at about 10 pJ/conversion. Hence, the power requirement of a 6 bit ADC @ 10 GHz is in the order of 6 W.

The second example relates to an amplifier whose FoM-value is expressed by:

$$FoM\_{ampl} = \frac{P}{\text{g }\text{ $\vec{B}$  CP}\_{1dB}} \quad \left[\text{Hz}^{-1}\right] \tag{9}$$

*P* - power dissipation of the amplifier; *g* - power gain in linear units; *B* - bandwidth; *CP*1*dB* - 1 dB compression point in linear units.

The FoM-approach can be extended to further electronic components and numerical algorithms as well. We can conclude two things from FoM-philosophy. Firstly, the designer of an electronic sub-system or algorithm has to achieve a reasonable small FoM-value with his design. Secondly, the designer of the whole system gets some hints on the feasibility of his system conception and the scope of its features if the corresponding FoM-values are known.

*Data throughput*: UWB sensors provide lots of data particularly if they are assigned for MIMO-systems and high measurement rate. In order to conserve energy, memory space and data transmission capacity, the sensors should not provide unnecessary data. We have six basic options to reduce the data throughput:

The data should be captured close to the Nyquist rate.

374 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

sensitivity to detect weak scattering targets in the vicinity of strong reflectors.

them here.

*Receiver efficiency <sup>r</sup>*

improvements.

with a certain rate *<sup>s</sup>*

*f* - sampling rate

*s*

known.

Jitter limits the performance of super resolution techniques and reduces the sensor

*Efficiency*: The term efficiency can be seen under different aspects. We will consider three of

receiver to exploit the incident signal energy. As the receiving signals are usually quite weak due to the restrictions of transmission power, one has to attach great importance to the receiver efficiency. It is determined by losses in the receiver front end, e.g. the insertion loss of filters or conversion loss of mixers or sampling gates. However, dead times for energy accumulation due to filter settling, incomplete data capture by reason of sub-sampling or incomplete exploitation of captured date due to serial instead of parallel data processing are much more important. Thus, the efficiency of recent UWB receivers is often reduced to values below 1 ‰ or even less which provides some potential for further

*Figure of Merit FoM* : In general terms, the Figure of Merit expresses the expense of energy which is required to achieve a certain effect. Two examples shall illustrate the approach. The first one deals with a Nyquist analog-to-digital converter which is aimed to digitize data

*f* . An obvious definition of the Figure of Merit can be:

*P* - power dissipation of the ADC; *ENOB* - effective number of bits of the ADC;

Typical FoM-values for high speed ADCs are to be found at about 10 pJ/conversion. Hence,

1 Hz *ampl dB*

*g B CP*

The FoM-approach can be extended to further electronic components and numerical algorithms as well. We can conclude two things from FoM-philosophy. Firstly, the designer of an electronic sub-system or algorithm has to achieve a reasonable small FoM-value with his design. Secondly, the designer of the whole system gets some hints on the feasibility of his system conception and the scope of its features if the corresponding FoM-values are

*s*

2 *flashADC ENOB*

*<sup>P</sup> FoM*

the power requirement of a 6 bit ADC @ 10 GHz is in the order of 6 W.

*CP*1*dB* - 1 dB compression point in linear units.

The second example relates to an amplifier whose FoM-value is expressed by:

*<sup>P</sup> FoM*

*P* - power dissipation of the amplifier; *g* - power gain in linear units; *B*

(see also (7)): The receiver efficiency describes the capability of the

J/conversion

1

*<sup>f</sup>* (8)

(9)



Without going into detail, we would like to mention at least some further aspects that influence the performance of sensor operation, too. They concern interference issues like robustness against jamming and low probability of intercept (LPIR- low probability of intercept radar).

The performance figures summarized above are the basis for deciding on a certain sensor configuration for a specific application. In what follows, the most popular UWB sensor principles will be tabulated and assessed with respect to the introduced performance figures.
