**2. Basics and state of the art**

This section provides an overview of state-of-the-art wireless positioning technologies. The section is divided into two subsections, with the first subsection describing measurement principles for positioning, whereas the second subsection has a focus on current positioning technologies based on RFID, particularly UHF RFID within the 900 MHz frequency band.

#### **2.1. Positioning measurement principles**

The first paragraph provides definitions for the terms precision, rightness and accuracy, whereas the following paragraphs describe the main positioning processes comprising lateration, angulation and fingerprinting. The last paragraph depicts the measurement techniques used for the positioning process, for instance, time of arrival, angle of arrival and received signal strength.

#### *2.1.1. Precision, rightness and accuracy*

Often, the terms "precision" and "accuracy" are used to define the same issue, namely how well a localization system or method works, e.g., the measurement error expressed in meters. However, precision and accuracy are not similar to each other. Therefore, this paragraph points out the differences and relations of the terms precision, rightness and accuracy.

**Precision** shows how well independent measurement values are located to each other. That means, if many measurement values are in dense proximity to each other, the measurement has a high precision; on the other hand, it does not mean that the measurement is accurate in any case. A standard term that is used to measure the precision is the standard deviation *σx* with

$$
\sigma\_x = \sqrt{E\left(\left(\hat{\mathbf{x}} - E\{\hat{\mathbf{x}}\}\right)^2\right)}\text{ and }\left.\hat{\sigma}\_x = \sqrt{\frac{1}{N-1}\sum\_{k=1}^N \left(\hat{\mathbf{x}}\_k - \overline{\hat{\mathbf{x}}}\right)^2}\right.\tag{1}
$$

$$\overline{\hat{\mathfrak{X}}} = \frac{1}{N} \sum\_{k=1}^{N} \hat{\mathfrak{x}}\_{k} \tag{2}$$

*σ* ^ *<sup>x</sup>* describes the estimated standard deviation of the measurement, *N* describes the number of measurements, *x* ^ *<sup>k</sup>* the measurement value at the *k*th measurement, the estimated mean value of the measurement values. *x* ^ describes the random variable of the measurement process, whereas E{⋅ } is the corresponding expectation value. In the following, the standard deviation *σx* is used as a measure for the precision of a positioning technique.

The chapter is organized as follows. Section 2 gives a brief overview of today's wireless position‐ ing technologies with a focus on RFID. Section 3 introduces the proposed positioning system and shows the theoretical approach along with an example. Section 4 focuses on challenges and limitations of the system and Section 5 presents results from measurements carried out underlin‐ ing the principle of operation. Section 6 provides a discussion based on the results. Finally, Section

This section provides an overview of state-of-the-art wireless positioning technologies. The section is divided into two subsections, with the first subsection describing measurement principles for positioning, whereas the second subsection has a focus on current positioning technologies based on RFID, particularly UHF RFID within the 900 MHz frequency band.

The first paragraph provides definitions for the terms precision, rightness and accuracy, whereas the following paragraphs describe the main positioning processes comprising lateration, angulation and fingerprinting. The last paragraph depicts the measurement techniques used for the positioning process, for instance, time of arrival, angle of arrival and

Often, the terms "precision" and "accuracy" are used to define the same issue, namely how well a localization system or method works, e.g., the measurement error expressed in meters. However, precision and accuracy are not similar to each other. Therefore, this paragraph points

**Precision** shows how well independent measurement values are located to each other. That means, if many measurement values are in dense proximity to each other, the measurement has a high precision; on the other hand, it does not mean that the measurement is accurate in any case.

out the differences and relations of the terms precision, rightness and accuracy.

A standard term that is used to measure the precision is the standard deviation *σx* with

{ } ( ) { } ( ) <sup>2</sup> <sup>2</sup>

*x x <sup>k</sup> <sup>k</sup> E E*

 s

<sup>1</sup> ˆ ˆ and <sup>ˆ</sup> , wi <sup>1</sup> <sup>ˆ</sup> <sup>ˆ</sup> th *<sup>N</sup>*

*N*

1 <sup>1</sup> ˆ ˆ *<sup>k</sup> <sup>k</sup> N x x*

*<sup>x</sup>* describes the estimated standard deviation of the measurement, *N* describes the number

1

*<sup>k</sup>* the measurement value at the *k*th measurement, the estimated mean

*<sup>N</sup>* <sup>=</sup> <sup>=</sup> å (2)

*x x x x* <sup>=</sup> =- = - å - (1)

7 gives a short summary and concludes with a perspective for future work.

**2. Basics and state of the art**

86 Radio Frequency Identification from System to Applications

**2.1. Positioning measurement principles**

received signal strength.

s

of measurements, *x*

^

*σ* ^

*2.1.1. Precision, rightness and accuracy*

**Rightness** or trueness describes how well the measured values respectively the expectation of the estimated values *x* ^ fit to the expectation of the true values *<sup>x</sup>*, i.e., a so called bias with

$$\text{Bias} = E\left\{\hat{\mathbf{x}} - \mathbf{x}\right\} = E\left\{\hat{\mathbf{x}}\right\} - \mathbf{x} \quad \text{and} \quad \widehat{Bias} = \overline{\hat{\mathbf{x}}} - \overline{\mathbf{x}} \tag{3}$$

Bias ^ is the estimated rightness of the measurement and *<sup>x</sup>* ¯ is the mean value of the true values. The rightness is a measure for the average discrepancy between a measured and a reference value and may be described as bias or offset.

**Accuracy** takes both, the precision and the rightness, into account. In fact, only high accuracy may be achieved if precision and rightness is high, too. A well known definition of the accuracy is the root mean square error RMSE, which is defined as

$$\text{RMSE} = \sqrt{\text{MSE}} = \sqrt{\text{E}\left[ (\hat{\mathbf{x}} \cdot \mathbf{x})^2 \right]} \text{ and } \text{RMSE} = \sqrt{\frac{1}{N} \sum\_{k=1}^{N} (\hat{\mathbf{x}}\_k \cdot \mathbf{x}\_k)^2} \tag{4}$$

RMSE ^ describes the estimated RMSE of the measurement and *xk* the true value at the the *k*th measurement.

According to [11] the first expression in Equation (4) can be transformed into

$$\text{RMSE} = \sqrt{\sigma\_{\text{x}}^2 + \text{Bias}^2} \tag{5}$$

Equation (5) shows that a distorted measurement with a high precision may be more accurate than an undistorted measurement with a low precision respectively standard deviation.

**Figure 1.** Example of trilateration with RFID reference tags

#### *2.1.2. Lateration*

Lateration is used to determine the position using distances to known reference points. For instance, an RFID reader may localize itself by evaluating distances to certain reference points, e.g., RFID tags, using the principle of trilateration, as shown in Figure 1. In this figure, twodimensional (2D) positioning of **P**, an RFID reader, can be realized using three reference points, here reference tags. Assuming the reader is able to exactly determine its distance *di* ∀*i* ∈{1,2, 3} to each of the tags, a circle is drawn around each tag with radius equal to the measured distance *di* . The intercept point of the three circles with radii *d*1…*d*3 indicates the position of the reader **P**. If the positions of the reference tags are known, the reader may determine its position by solving the set of equations

$$\sqrt{(\mathbf{x}\_{\mathrm{P}} \cdot \mathbf{x}\_{i})^{2} + (y\_{\mathrm{P}} \cdot y\_{i})^{2}} = d\_{i}, \quad i \in \{1, 2, 3\}. \tag{6}$$

nization between transmitter and receiver, the clock offset *τ*offset will lead to a constant distance error *d*offset within each range measurement. This additional parameter can be solved by adding one more equation (equal to one additional tag) to the minimum number of equations when

As mentioned before, there should be no time offset in RFID systems. Nevertheless, constant phase shifts due to the non-constant reflection coefficient of RFID tags [16] can lead to an additional offset distance *d*offset, having the same effect as a time-based clock offset. The set of equations in (11) describe hyperbolas rather than circles around the reference points. Figure 2 shows the effect of an offset distance *d*offset and two out of four hyperbolic curves, which

The principle of angulation rests upon the relations between angles and distances within a triangle; therefore, it is mostly common under the term triangulation. If two angles and one side of a triangle are known the remaining distances respectively the position to be determined can be calculated using the *law of sines* and the *angle sum of a triangle*. Figure 3 shows the principle used: Two antennas (Ant. #1 and Ant. #2) of an RFID reader are deployed to calculate the position of the RFID tag. This can be realized using, for instance, phase-based or directiondefined measurements. From independent angle measurements one obtains the angles *α* and *β*; the distance *d*0 is known in advance. Subsequently, the remaining angle *γ* is calculated (angle sum in triangle) and from that the missing two distances *d*<sup>1</sup> and *d*<sup>2</sup> from the antennas to the

RFID tag (law of sines). Angulation may be used in 2D or 3D localization problems.

)<sup>2</sup> + *d*offset=*di* ∀*i* ∈{1,2, 3,4} (11)

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there is no synchronization error:

would intercept in position P.

*2.1.3. Angulation*

(*x*<sup>P</sup> - *xi*

)<sup>2</sup> + (*y*<sup>P</sup> - *yi*

**Figure 2.** Example of hyperbolic lateration using RFID tags as reference points

(*x*P; *y*P) is the position of the reader, which shall be estimated and (*x*<sup>i</sup> ; *y*i ) ∀*i* ∈{1,2, 3} is the position of each of the reference points respectively tags. Solving the set of equations in (6) for three reference points yields [12, 13]:

$$
\begin{pmatrix} \mathbf{x}\_{\rm P} \\ \mathbf{y}\_{\rm P} \end{pmatrix} = \begin{pmatrix} a\_{1,2} & b\_{1,2} \\ a\_{1,3} & b\_{1,3} \end{pmatrix}^{-1} \begin{pmatrix} \mathcal{g}\_{1,2} \\ \mathcal{g}\_{1,3} \end{pmatrix} \tag{7}
$$

with

$$a\_{1,i} = \mathbf{2}(\mathbf{x}\_i - \mathbf{x}\_1), \quad i \in \{\mathbf{2}, \mathbf{3}\} \tag{8}$$

$$b\_{1,i} = 2(y\_i - y\_1), \quad i \in \{2, 3\} \tag{9}$$

and

$$\mathbf{g}\_{1,i} = d\_1^2 \ -d\_i^2 \ -\left(\mathbf{x}\_1^2 + y\_1^2\right) \ \cdot \left(\mathbf{x}\_i^2 + y\_i^2\right) \ \prime \ i \in \{2, 3\}. \tag{10}$$

In the case of three-dimensional (3D) positioning, a minimum of four reference points is necessary to unambiguously determine the exact position. However, due to the imperfectness of the distance measurement (noise, fading channel, etc.), there is usually no exact interception point, but rather an intersection area. Therefore, different error-minimizing algorithms can be used to make a best estimate for the position determination [14]. The accuracy of the meas‐ urements can be further increased by making use of more than the necessary minimum of reference points [15].

In RFID, generally, there exists clock synchronization between transmitter and receiver, as both components are located within the RFID reader. If, however, there is no clock synchro‐ nization between transmitter and receiver, the clock offset *τ*offset will lead to a constant distance error *d*offset within each range measurement. This additional parameter can be solved by adding one more equation (equal to one additional tag) to the minimum number of equations when there is no synchronization error:

$$\sqrt{(\mathbf{x}\_{\text{P}} \cdot \mathbf{x}\_{i})^{2} + (y\_{\text{P}} \cdot y\_{i})^{2}} + d\_{\text{offset}} = d\_{i} \quad \forall \; i \in \{1, 2, 3, 4\} \tag{11}$$

As mentioned before, there should be no time offset in RFID systems. Nevertheless, constant phase shifts due to the non-constant reflection coefficient of RFID tags [16] can lead to an additional offset distance *d*offset, having the same effect as a time-based clock offset. The set of equations in (11) describe hyperbolas rather than circles around the reference points. Figure 2 shows the effect of an offset distance *d*offset and two out of four hyperbolic curves, which would intercept in position P.

**Figure 2.** Example of hyperbolic lateration using RFID tags as reference points

#### *2.1.3. Angulation*

*2.1.2. Lateration*

measured distance *di*

with

and

reference points [15].

determine its position by solving the set of equations

88 Radio Frequency Identification from System to Applications

*g*1,*<sup>i</sup>* =*d*<sup>1</sup>

<sup>2</sup> - *di*

<sup>2</sup> - (*x*<sup>1</sup>

<sup>2</sup> <sup>+</sup> *<sup>y</sup>*<sup>1</sup> 2 ) - (*xi*

<sup>2</sup> <sup>+</sup> *yi* 2

In the case of three-dimensional (3D) positioning, a minimum of four reference points is necessary to unambiguously determine the exact position. However, due to the imperfectness of the distance measurement (noise, fading channel, etc.), there is usually no exact interception point, but rather an intersection area. Therefore, different error-minimizing algorithms can be used to make a best estimate for the position determination [14]. The accuracy of the meas‐ urements can be further increased by making use of more than the necessary minimum of

In RFID, generally, there exists clock synchronization between transmitter and receiver, as both components are located within the RFID reader. If, however, there is no clock synchro‐

three reference points yields [12, 13]:

(*x*<sup>P</sup> - *xi*

)<sup>2</sup> + (*y*<sup>P</sup> - *yi*

(*x*P; *y*P) is the position of the reader, which shall be estimated and (*x*<sup>i</sup>

(*x*P *y*P ) =(

Lateration is used to determine the position using distances to known reference points. For instance, an RFID reader may localize itself by evaluating distances to certain reference points, e.g., RFID tags, using the principle of trilateration, as shown in Figure 1. In this figure, twodimensional (2D) positioning of **P**, an RFID reader, can be realized using three reference points, here reference tags. Assuming the reader is able to exactly determine its distance *di* ∀*i* ∈{1,2, 3} to each of the tags, a circle is drawn around each tag with radius equal to the

position of the reader **P**. If the positions of the reference tags are known, the reader may

position of each of the reference points respectively tags. Solving the set of equations in (6) for

*a*1,2 *b*1,2 *a*1,3 *b*1,3 ) -1 ( *g*1,2 *g*1,3

. The intercept point of the three circles with radii *d*1…*d*3 indicates the

)<sup>2</sup> =*di* , *i* ∈{1,2, 3}. (6)

*a*1,*<sup>i</sup>* =2(*xi* - *x*1), *i* ∈{2,3} (8)

*b*1,*<sup>i</sup>* =2(*yi* - *y*1), *i* ∈{2,3} (9)

; *y*i

), (7)

), *i* ∈{2,3}. (10)

) ∀*i* ∈{1,2, 3} is the

The principle of angulation rests upon the relations between angles and distances within a triangle; therefore, it is mostly common under the term triangulation. If two angles and one side of a triangle are known the remaining distances respectively the position to be determined can be calculated using the *law of sines* and the *angle sum of a triangle*. Figure 3 shows the principle used: Two antennas (Ant. #1 and Ant. #2) of an RFID reader are deployed to calculate the position of the RFID tag. This can be realized using, for instance, phase-based or directiondefined measurements. From independent angle measurements one obtains the angles *α* and *β*; the distance *d*0 is known in advance. Subsequently, the remaining angle *γ* is calculated (angle sum in triangle) and from that the missing two distances *d*<sup>1</sup> and *d*<sup>2</sup> from the antennas to the RFID tag (law of sines). Angulation may be used in 2D or 3D localization problems.

ToA measurements directly determine the distance by using the time of flight *t*ToA of the signal. Multiplied with the corresponding propagation speed *c*, the speed of light in case of electro‐ magnetic waves, this results directly in the distance *d*ToA between transmitter and receiver as described in Equation (12). ToA measurements can be used directly along with trilateration

TDoA measurements determine the time difference of a signal received at known reference points rather than measuring directly the time between transmitter and receiver. This means, that the time stamp of the signal transmitted via the object to be localized is unknown, but the time differences at the synchronized receivers are determined. In contrast to ToA, TDoA does not require any synchronization between transmitter and receiver. The reference stations must be synchronized, indeed. One positioning method using TDoA measurements is hyperbolic

RSS (Received Signal Strength) measurements are based on the received signal strength at the receiver. Hence, there are two possible candidates to process RSS-based data. The first one is based on the propagation conditions, usually including a modified and enhanced form of Friis

*d*0

Equation (13) describes the free space attenuation formula depending on the distance *d* and wavelength *λ* with receiving power *Pr*, transmitting power *Pt*, receiving and transmitting

on the other hand describes the path loss PL(*d*) depending on the distance *d* related to a reference path loss PL(*d*0)at distance *d*0. The path loss may be described as the difference of transmitted and received power in dB. *α* represents the path loss exponent that depends on the propagation environment, whereas *X* is a zero-mean Gaussian distributed random variable describing the fading effects at different locations and instants of time. If, in case of the usage of Equation (14), PL(*d*0), *α* and the variance of *X* is known, one can calculate directly the probability for a certain distance *d* between transmitter and receiver. One disadvantage is that *α* and *X* are very dependent on the environment and can change significantly. The RSS

*Pr* <sup>=</sup>*Pt* <sup>+</sup> *Gt* <sup>+</sup> *Gr* <sup>+</sup> 20log ( *<sup>λ</sup>*

PL(*d*)=PL(*d*0) <sup>+</sup> <sup>10</sup>*α*log ( *<sup>d</sup>*

antenna gains *Gt* and *Gr* together with the free space attenuation ( *<sup>λ</sup>*

measurements can be used along with lateration methods.

*d*ToA =*t*ToA ⋅ *c* (12)

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<sup>4</sup>*π<sup>d</sup>* ) dB , (13)

) + *X* dB . (14)

<sup>4</sup>*π<sup>d</sup>* )<sup>2</sup>

in dB. Equation (14)

methods.

lateration (see Paragraph 2.1.2).

e.g., the log-distance path loss model

transmission equation

**Figure 3.** Triangulation example using two antennas to determine the position of an RFID tag

#### *2.1.4. Scene-based localization / fingerprinting*

Scene-based localization is divided in two sequential processes, a calibration process and an operational process. The calibration process records any environmental values (optical, electrical, physical, etc.), also known as fingerprints, at several positions within a scene and stores the data in a database [17, 18]. The following operational process is thus able to determine the position by measuring the current environmental values and comparing them with the values in the database. Special algorithms estimate the position by finding the position with the minimal error [19]. Figure 4 shows a room map with different WLAN base stations showing the electrical field strength at different locations [20] used along with WLAN positioning.

**Figure 4.** Electrical field strength distribution within a building to be used for WLAN positioning [20]

#### *2.1.5. Positioning measurement techniques*

After highlighting the measurement principles, this paragraph gives a brief overview over the common technologies used. Distance measurements may be based on measuring the time of flight, the signal strength and the phase between transmitted and received signal.

Time measurements include Time of Arrival (ToA) and Time Difference of Arrival (TDoA) measurements.

ToA measurements directly determine the distance by using the time of flight *t*ToA of the signal. Multiplied with the corresponding propagation speed *c*, the speed of light in case of electro‐ magnetic waves, this results directly in the distance *d*ToA between transmitter and receiver as described in Equation (12). ToA measurements can be used directly along with trilateration methods.

$$d\_{\rm ToA} = t\_{\rm ToA} \cdot c \tag{12}$$

TDoA measurements determine the time difference of a signal received at known reference points rather than measuring directly the time between transmitter and receiver. This means, that the time stamp of the signal transmitted via the object to be localized is unknown, but the time differences at the synchronized receivers are determined. In contrast to ToA, TDoA does not require any synchronization between transmitter and receiver. The reference stations must be synchronized, indeed. One positioning method using TDoA measurements is hyperbolic lateration (see Paragraph 2.1.2).

RSS (Received Signal Strength) measurements are based on the received signal strength at the receiver. Hence, there are two possible candidates to process RSS-based data. The first one is based on the propagation conditions, usually including a modified and enhanced form of Friis transmission equation

$$P\_r = P\_t + G\_t + G\_r + 20\log\left(\frac{\lambda}{4\pi d}\right) \text{[dB]}\tag{13}$$

e.g., the log-distance path loss model

**Figure 3.** Triangulation example using two antennas to determine the position of an RFID tag

**Figure 4.** Electrical field strength distribution within a building to be used for WLAN positioning [20]

flight, the signal strength and the phase between transmitted and received signal.

After highlighting the measurement principles, this paragraph gives a brief overview over the common technologies used. Distance measurements may be based on measuring the time of

Time measurements include Time of Arrival (ToA) and Time Difference of Arrival (TDoA)

Scene-based localization is divided in two sequential processes, a calibration process and an operational process. The calibration process records any environmental values (optical, electrical, physical, etc.), also known as fingerprints, at several positions within a scene and stores the data in a database [17, 18]. The following operational process is thus able to determine the position by measuring the current environmental values and comparing them with the values in the database. Special algorithms estimate the position by finding the position with the minimal error [19]. Figure 4 shows a room map with different WLAN base stations showing the electrical field strength at different locations [20] used along with WLAN positioning.

*2.1.4. Scene-based localization / fingerprinting*

90 Radio Frequency Identification from System to Applications

*2.1.5. Positioning measurement techniques*

measurements.

$$\text{PL} \left( d \right) = \text{PL} \left( d\_0 \right) + 10\alpha \log \left( \frac{d}{d\_0} \right) + X \text{ [dB]}.\tag{14}$$

Equation (13) describes the free space attenuation formula depending on the distance *d* and wavelength *λ* with receiving power *Pr*, transmitting power *Pt*, receiving and transmitting antenna gains *Gt* and *Gr* together with the free space attenuation ( *<sup>λ</sup>* <sup>4</sup>*π<sup>d</sup>* )<sup>2</sup> in dB. Equation (14) on the other hand describes the path loss PL(*d*) depending on the distance *d* related to a reference path loss PL(*d*0)at distance *d*0. The path loss may be described as the difference of transmitted and received power in dB. *α* represents the path loss exponent that depends on the propagation environment, whereas *X* is a zero-mean Gaussian distributed random variable describing the fading effects at different locations and instants of time. If, in case of the usage of Equation (14), PL(*d*0), *α* and the variance of *X* is known, one can calculate directly the probability for a certain distance *d* between transmitter and receiver. One disadvantage is that *α* and *X* are very dependent on the environment and can change significantly. The RSS measurements can be used along with lateration methods.

The second RSS-based approach is to measure in advance RSS values at certain positions within the localization area (fingerprints). The measured values are pre-processed and stored into a database. During the proper localization process, the current measurement values are compared to the values in the database and a best-fit position, based on the current values, is estimated. The advantage of using RSS values for this approach is that almost all devices come along with some kind of RSS-based output, including RFID readers. This method is used in scene-based positioning techniques.

Phase measurements can be used to provide information about speed, distance and angle. A good overview over these techniques is given in [21]. The radial velocity *v* of a tag is measured by evaluating the phase shift ∂*φ* during different moments in time ∂*t* as given in Equation (15).

$$
\Delta \sigma = -\frac{c}{2\omega\_0} \frac{\partial \varphi}{\partial t} \tag{15}
$$

between the RSS value and the position is based on the indoor channel model from Seidel and Rappaport [26]. The accuracy of the SpotON system is given with a cube of 3 m edge length, but this is dependent on the number of reference tags used. A disadvantage of the system is the long position calculation time from 10 to 20 s; an advantage is the easy to extend infra‐

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A 2.4 GHz RFID system based on SAW transponders is described in [27]. The SAW tags use a bandwidth of 40 MHz and reduce the echoes from the environment as the reflected tag signal is delayed due to the lower surface speed on the SAW material. The signal time on the SAW transponder is *T*SAW =2.2 µs; so the reflections and echoes from the reader are almost faded out before the SAW-reflected signal responses back to the reader. A three-antenna system is used to perform a 2D positioning. However, the localization accuracy is strongly temperature-

A localization system in the 5.8 GHz frequency band is described in [28]. The system is build upon active transponders and multiple base stations. One reference transponder is used as wireless synchronization source for the base stations. The system operates on the FMCW (frequency modulated continuous wave) principle (see [29]) and evaluates the time difference of a measurement transponder signal to determine the position of the measurement trans‐

The principle of FMCW is used to measure the distance to a certain object. The idea behind FMCW is to sweep a frequency band with the sweep rate *α* and record the phase and frequency differences. Furthermore, the transmitted signal from the reader is modulated by the trans‐ ponder with a modulation frequency *f* mod. The usage of a modulation frequency shifts the measurement signal into a higher frequency band (by *f* mod), in order to suppress certain disturbances and noise within the baseband. The distance *d* is calculated through the frequency difference Δ *f* and the phase difference Δ*φ* [30], with the latter providing a high range resolution within half a wavelength of the signal. Therefore, Δ *f* provides a coarse distance estimation and Δ*φ* a more accurate one. Δ*φ* alone cannot be used as direct distance estimation due to ambiguities of the phase information. According to [30] the distance to a transponder

<sup>2</sup> <sup>⋅</sup> *<sup>α</sup>* and *d*precise<sup>=</sup> *<sup>c</sup>* ⋅Δ*<sup>φ</sup>*

[31] describes an FMCW-based RFID system using a transponder with an UHF front-end working at 868 MHz. The transponder IC provides a modulation frequency of

<sup>4</sup> <sup>⋅</sup> *<sup>ω</sup>*<sup>0</sup> . (18)

dependent and adds up to around 20 cm in a room with the dimension 2 m × 2 m.

ponder. The position accuracy is given with 10 cm on an area of 500 m × 500 m.

*<sup>d</sup>*coarse<sup>=</sup> *<sup>π</sup>* <sup>⋅</sup> *<sup>c</sup>* ⋅Δ *<sup>f</sup>*

structure and low system costs.

*2.2.2. ToA-based range estimation*

*2.2.3. TDoA-based range estimation*

*2.2.4. Phase-based range estimation*

can be calculated with

with *c* being the propagation speed and *ω*0 the fixed circular frequency. The distance *d* between a tag and a reader can be calculated according to Equation (16) by measuring the phase shift at different frequencies.

$$d = -\frac{c}{4\pi} \frac{\partial \phi}{\partial f} \tag{16}$$

Finally, phase measurements may be used to measure the angle *θ* between reader and tag (Angle of Arrival, AoA) using multiple receiving antennas. For two receiving antennas, Equation (17) describes the relation between the incoming angle *θ*, the phase difference *φ*<sup>2</sup> - *φ*<sup>1</sup> at a certain carrier frequency, and the spacing *a* between the receiving antennas.

$$\Theta \approx \sin^{-1}\left[ -\frac{c}{\omega} \stackrel{\varphi\_2 \ -\varphi\_1}{\omega} \right] \tag{17}$$

Phase measurement are used along with lateration and angulation principles to calculate the distance between transmitter and receiver respectively reader and tag.

#### **2.2. Survey on UHF RFID-based localization systems**

The following paragraphs provide a brief survey on state-of-the-art RFID localization systems within the UHF and microwave frequency band. The survey includes systems using RSS values, ToA and TDoA measurements, phase-based measurements as well as fingerprinting methods. Further surveys are provided in [22, 23, 24].

#### *2.2.1. RSS-based direct range estimation*

The SpotON system [25] is based on active RFID tags (working at 916.5 MHz) and provides a 3D ad hoc localization. RFID readers measure the signal strength of active RFID tags and a central server performs the calculation of the position within the environment. The relation between the RSS value and the position is based on the indoor channel model from Seidel and Rappaport [26]. The accuracy of the SpotON system is given with a cube of 3 m edge length, but this is dependent on the number of reference tags used. A disadvantage of the system is the long position calculation time from 10 to 20 s; an advantage is the easy to extend infra‐ structure and low system costs.

#### *2.2.2. ToA-based range estimation*

The second RSS-based approach is to measure in advance RSS values at certain positions within the localization area (fingerprints). The measured values are pre-processed and stored into a database. During the proper localization process, the current measurement values are compared to the values in the database and a best-fit position, based on the current values, is estimated. The advantage of using RSS values for this approach is that almost all devices come along with some kind of RSS-based output, including RFID readers. This method is used in

Phase measurements can be used to provide information about speed, distance and angle. A good overview over these techniques is given in [21]. The radial velocity *v* of a tag is measured by evaluating the phase shift ∂*φ* during different moments in time ∂*t* as given in Equation (15).

with *c* being the propagation speed and *ω*0 the fixed circular frequency. The distance *d* between a tag and a reader can be calculated according to Equation (16) by measuring the phase shift

Finally, phase measurements may be used to measure the angle *θ* between reader and tag (Angle of Arrival, AoA) using multiple receiving antennas. For two receiving antennas, Equation (17) describes the relation between the incoming angle *θ*, the phase difference *φ*<sup>2</sup> - *φ*<sup>1</sup>

<sup>∂</sup> *<sup>t</sup>* (15)

<sup>∂</sup> *<sup>f</sup>* (16)

*<sup>a</sup>* (17)

*<sup>v</sup>* <sup>=</sup> - *<sup>c</sup>* 2*ω*<sup>0</sup> ∂*φ*

*<sup>d</sup>* <sup>=</sup> - *<sup>c</sup>* 4*π* ∂*φ*

at a certain carrier frequency, and the spacing *a* between the receiving antennas.

*<sup>θ</sup>* <sup>≈</sup>sin-1 - *<sup>c</sup>*

distance between transmitter and receiver respectively reader and tag.

**2.2. Survey on UHF RFID-based localization systems**

methods. Further surveys are provided in [22, 23, 24].

*2.2.1. RSS-based direct range estimation*

*ω*

Phase measurement are used along with lateration and angulation principles to calculate the

The following paragraphs provide a brief survey on state-of-the-art RFID localization systems within the UHF and microwave frequency band. The survey includes systems using RSS values, ToA and TDoA measurements, phase-based measurements as well as fingerprinting

The SpotON system [25] is based on active RFID tags (working at 916.5 MHz) and provides a 3D ad hoc localization. RFID readers measure the signal strength of active RFID tags and a central server performs the calculation of the position within the environment. The relation

*φ*<sup>2</sup> - *φ*<sup>1</sup>

scene-based positioning techniques.

92 Radio Frequency Identification from System to Applications

at different frequencies.

A 2.4 GHz RFID system based on SAW transponders is described in [27]. The SAW tags use a bandwidth of 40 MHz and reduce the echoes from the environment as the reflected tag signal is delayed due to the lower surface speed on the SAW material. The signal time on the SAW transponder is *T*SAW =2.2 µs; so the reflections and echoes from the reader are almost faded out before the SAW-reflected signal responses back to the reader. A three-antenna system is used to perform a 2D positioning. However, the localization accuracy is strongly temperaturedependent and adds up to around 20 cm in a room with the dimension 2 m × 2 m.

#### *2.2.3. TDoA-based range estimation*

A localization system in the 5.8 GHz frequency band is described in [28]. The system is build upon active transponders and multiple base stations. One reference transponder is used as wireless synchronization source for the base stations. The system operates on the FMCW (frequency modulated continuous wave) principle (see [29]) and evaluates the time difference of a measurement transponder signal to determine the position of the measurement trans‐ ponder. The position accuracy is given with 10 cm on an area of 500 m × 500 m.

#### *2.2.4. Phase-based range estimation*

The principle of FMCW is used to measure the distance to a certain object. The idea behind FMCW is to sweep a frequency band with the sweep rate *α* and record the phase and frequency differences. Furthermore, the transmitted signal from the reader is modulated by the trans‐ ponder with a modulation frequency *f* mod. The usage of a modulation frequency shifts the measurement signal into a higher frequency band (by *f* mod), in order to suppress certain disturbances and noise within the baseband. The distance *d* is calculated through the frequency difference Δ *f* and the phase difference Δ*φ* [30], with the latter providing a high range resolution within half a wavelength of the signal. Therefore, Δ *f* provides a coarse distance estimation and Δ*φ* a more accurate one. Δ*φ* alone cannot be used as direct distance estimation due to ambiguities of the phase information. According to [30] the distance to a transponder can be calculated with

$$d\_{\text{coarse}} = \frac{\pi \cdot c \cdot \Lambda f}{2 \cdot \alpha} \text{ and } d\_{\text{prerise}} = \frac{c \cdot \Lambda \rho}{4 \cdot \omega\_0}. \tag{18}$$

[31] describes an FMCW-based RFID system using a transponder with an UHF front-end working at 868 MHz. The transponder IC provides a modulation frequency of

*f* mod =300 kHz and is driven by a 2.45 GHz FMCW signal with a bandwidth of 75 MHz. The system is tested on a cable-based setup and delivers an RMSE of 1 cm with cable lengths between 1 m and 9.5 m.

The system in [32] uses the phase difference observed at different frequencies to estimate the range between transponder and reader. The range estimation is performed according to Equation (16), whereas the maximum range *d*max due to phase ambiguities is given with

$$d\_{\text{max}} = \frac{c}{2\text{B}}\,. \tag{19}$$

accurate positioning is realized when RSS values are used along with read rates of the transponders. Within the calibration phase, one tries to generate a high amount of reference points (fingerprints). Two algorithms are used and compared to perform within the position‐ ing phase, a cascaded algorithm and a kNN algorithm. The cascaded algorithm runs the rough localization followed by the kNN algorithm for the high accuracy. The second algorithm resigns to use the rough position estimation. Similarly, the RSS-based fingerprints perform better than the read rates. Dependent on the environment, positioning errors between 37.9 cm

Localizing with Passive UHF RFID Tags Using Wideband Signals

http://dx.doi.org/10.5772/53769

95

This section introduces a brief motivation for the realized RFID positioning system before

As derived from Section 2, current passive RFID localization systems suffer either from a high effort in the calibration phase (fingerprinting) or from bandwidth limitations which hold down the system's overall accuracy. Higher accuracies may be achieved using phase-based ap‐ proaches at the expense of more complex hardware structures and necessary volume (see, for instance, phased array antennas [43]), only usable for fixed reader hardware. Therefore, an

The here proposed system offers high bandwidth, but with very low power, and is based on a ToA method performing direct position estimation. As a consequence, additional hardware effort is necessary to provide the generation and evaluation of the high bandwidth signals.

In the following, a brief overview of the system, particularly its principle working structure,

Assuming a scenario as given in Figure 5. The scenario consists of *n* tags, whereby the distance to the *i*th tag has to be evaluated. The RFID reader is indicated at the bottom (only the coupler with antenna in monostatic mode) with input signal *x*reader (into the antenna) and output signal *y*reader (from the antenna). s1 to sn describe the backscatter modulation factors of the trans‐ ponders, i.e., the factor with which the incoming signal from the reader is reflected with (principle of backscatter). If this factor is one, the complete signal is backscattered to the reader. Indeed, data from tag to reader is transmitted by varying this factor in time with the data to be sent [10, 44]. h1 to hn describe the bidirectional channel impulse responses between reader and tags. For reasons of simplification the following equations and terms are written without

and 42.1 cm may be achieved.

**•** no change in hardware,

**•** direct position estimation.

**•** high bandwidth,

is provided.

**3. Wideband UHF RFID positioning system**

ideal passive mobile RFID positioning system should have:

using the time *t*, although the expressions depend on it.

describing the basic structure of the system.

However, the choice of the bandwidth B strongly influences the system's capabilities. A high B generates a high accuracy but a low maximum range; a low B leads to a higher range but at the expenses of a lower accuracy. Simulations at an SNR of 10 dB results in errors of 2.5 m for a frequency separation of B=1 MHz, and errors of 0.1 m for a B of 26 MHz. One has to keep in mind that the separation of 26 MHz is only valid within the US frequency band for RFID that ranges from around 902 MHz to 928 MHz. The European band is smaller (865.6 MHz to 867.6 MHz) leading to a lower accuracy.

#### *2.2.5. Scene-based range estimation*

LANDMARC [33] is an extension and improvement of the SpotON system [25, 34]. The system consists of fixed RFID readers, active reference tags (landmarks) and tags to be localized. The system uses RSS values connected with the kNN (k-nearest neighbor) algorithm [35] to estimate the position. The average error of the system is given with 1 m [33].

[36] examines the localization error of the LANDMARC system using passive, instead of active RFID tags. As a result, the orientation of the tags has a major influence on the total performance of the system. Using the kNN algorithm, in 47.5 % of the cases, the error was less than 0.5 m and in 27.5 % of the cases, the error was less than 0.3 m. However, in comparison to the original LANDMARC system, the overall range is smaller due to the usage of passive RFID technology.

A system based on a particle filter is proposed in [37]. It uses two RFID readers mounted on a small mobile vehicle to localize itself using RSS values. The calibration phase is performed in a room of size 5 m × 10 m. Depending on the speed of the vehicle and the material on which the tags are located (plastics, concrete, metal) the average error is between 1.35 cm and 2.48 cm. This system is based on the mobile robot system in [38] that incorporates a SLAM algorithm [39] based on Monte Carlo methods [40].

[41] describes a positioning system using fingerprints (RSS values and read rate) to localize tagged objects. First, a rough positioning is done using antenna cells, with each antenna illuminating a different room zone. This rough classification is realized using either Bayesian filter, kernel density estimation (KDE) based measurement models, support vector machines (SVM) or LogitBoost [42]. RSS-based values and read rate is used along with the algorithms to roughly estimate the position of the tagged object. One result was that the estimations based on RSS values perform better than the estimations based on the read rate. An even more accurate positioning is realized when RSS values are used along with read rates of the transponders. Within the calibration phase, one tries to generate a high amount of reference points (fingerprints). Two algorithms are used and compared to perform within the position‐ ing phase, a cascaded algorithm and a kNN algorithm. The cascaded algorithm runs the rough localization followed by the kNN algorithm for the high accuracy. The second algorithm resigns to use the rough position estimation. Similarly, the RSS-based fingerprints perform better than the read rates. Dependent on the environment, positioning errors between 37.9 cm and 42.1 cm may be achieved.
