A Compact UWB Indoor and Through-Wall Radar with Precise Ranging and Tracking

Abstract

Ultrawideband (UWB) technology has many advantages compared to its narrowband counterpart in many applications. We present a new compact low-cost UWB radar for indoor and through-wall scenario. The focus of the paper is on the development of the signal processing algorithms for ranging and tracking, taking into account the particular properties of the UWB CMOS transceiver and the radiation characteristics of the antennas. Theoretical analysis for the algorithms and their evaluations by measurements are presented in the paper. The ranging resolution of this UWB radar has achieved 1-2 mm RMS accuracy for a moving target in indoor environment over a short range, and Kalman tracking algorithm functions well for the through-wall detection.

1. Introduction

Due to its quite unique properties, ultrawideband (UWB) technology finds many applications in different areas, such as UWB sensor network for precise ranging and geolocation, UWB radar and imaging systems with superior penetration and high resolution, and super sensitive UWB radio astronomy [1–3].

From the radar perspective, UWB systems exhibit several distinct advantages over the narrowband counterparts [4, 5]: (1) higher ranging resolution with higher penetration ability through lossy materials simultaneously; (2) enhanced target recognition; (3) better penetration ability to passive interference (i.e., rain, fog, and clutter); (4) very low cost, particularly, the Novelda UWB radar system used in this paper.

We present a new compact and low-cost UWB radar system for ranging and tracking of moving objects in indoor and through-wall environments. The UWB radar consists of a commercially available Novelda R2A UWB CMOS chip transceiver [6] and the recently developed UWB 2–15-GHz self-grounded-Bow-Tie (SGBT) antennas [7, 8]; see Figure 1. The particular properties of the Novelda transceiver and the radiation characteristics of the antenna have been taken into account for the development of the ranging and tracking algorithms.

Figure 1 

The compact UWB radar system for indoor and through-wall ranging and tracking.

The ranging resolution of this UWB radar by applying the algorithm has achieved an accuracy of 1-2 mm RMS error for a moving target in indoor environments over a short range, and the Kalman filter tracking algorithm functions well for a through-wall detection.

In this paper, the system is briefed in Section 2. The self-grounded Bow-Tie antenna is described in Section 2.2. The system signal is modeled in Section 3. Algorithms of adaptive clutter mapping are presented in Section 4. Afterwards, the algorithms for ranging are described in detail in Section 5. An important issue, calibration of the system, is discussed in Section 6. A Kalman tracking algorithm based on the pulse-spectrum ranging is briefed in Section 7. Then, the evaluation of the algorithms by measurements is illustrated in Section 8.

2. The Radar System

2.1. Overview of the System

The UWB radar system consists of two SGBT antennas and one Novelda transceiver; see Figure 1. Pulses are transmitted via one antenna (Tx antenna), reflected by targets, and then received by the second antenna (Rx antenna), as depicted in Figure 2.

Figure 2 

Presentation of the operating principle of the compact UWB radar system.

For memory efficiency, each measurement by the Novelda transceiver is taken within a fixed time frame (referred to as the measurement frame, or simply the frame), with an off-set time delay predefined by user. Each measurement has 128 samples. The time interval (or step) between two consecutive samples is 27.8 ps, corresponding to the sample resolution of 4.167 mm in the wave propagation direction. The duration of one frame is then 127×27.8ps=3.53ns in time and 529 mm in space accordingly.

The received signals can be amplified by user-defined values in the transceiver. The signal sample data are then sent to the microcontroller for data processing.

2.2. The Self-Grounded Bow-Tie Antenna

The self-grounded Bow-Tie (SGBT) antenna, shown in Figure 1, is a new type of UWB antenna, developed recently at Chalmers University of Technology [7, 8]. Several other types of UWB antennas have been also developed at Chalmers for different applications [9–12].

The SGBT antenna is chosen for this UWB radar system due to the following characteristics: (1) very good time response; (2) compact size and low profile; (3) UWB constant directional radiation beam; (4) good UWB reflection coefficient; (5) better penetration ability than other UWB antennas [8, 13]. Antipodal Vivaldi antennas were used in the previous work [14]. With the use of the SGBT antenna, the performance of the UWB radar system has been improved, due to its good time response and UWB constant directional radiation beam.

The size of the SGBT antenna is 54×58×24mm³, with the operating frequency band covering 2–15 GHz. The details of the measured and simulated performance of the antenna can be found in [7, 8].

The comparison between the self-grounded Bow-Tie antenna and a Vivaldi antenna with a size of 135×145×2mm³ on the penetration ability through a concrete wall was presented in [8], which indicated that the system using the self-grounded Bow-Tie antenna has 6 dB higher signal level that is using the Vivaldi antenna. The antipodal Vivaldi antennas used in [14] have even lower penetration ability than the aforementioned Vivaldi antenna.

The measured time-domain response of the UWB radar system is shown in Figure 3, when the two antennas are separated by a distance of 250 mm in the face-to-face configuration in an anechoic chamber. From the figure, two time-response measures, the width of the pulse full-width at half-maximum (FWHM) 𝜏_FWHM and the duration of the ringing 𝜏_𝑟=0.22, can be obtained as 𝜏_FWHM=134ps and 𝜏_𝑟=0.22=260ps, which states a fast time pulse response of the radar system. Please refer to [15] for the definitions of 𝜏_FWHM and 𝜏_𝑟=0.22.

(a)

(b)

Figure 3 

Measured time-domain impulse response of the UWB radar system when the two antennas are separated by 250 mm in the face-to-face configuration.

Note that in the antenna setup of the system, it is required that the target should be a certain distance away from the antennas for this radar system to have an accurate ranging, because then the target pulse can be distinguished clearly from the direct-coupling pulse between the antennas. Since the pulse used in this work has a width of about 0.5 ns (Figure 3), which corresponds to 150 mm, the distance between the target and the antennas should be larger than 150 mm. With enough margin, it is set in this work that the minimum distance between the target and the antennas should be larger than both the distance between the two antennas and 200 mm for an accurate ranging result.

3. Signal Model

Figure 4 shows the signal model used in this work, where the received signal 𝐫 consists of the clutter 𝐫_𝑒 (unwanted reflected signal from a static indoor environment, such as a wall), the target signal 𝐫_𝑡 that is reflected from moving targets, and the noise 𝐞. Note that the definitions of the clutter and the target can be different in different applications. The noise 𝐞 is defined as the channel noise, modeled by a zero-mean random variable. Here, we assume that there is no jamming, namely, no other sources of signals within the field of the radar system.

Figure 4 

Signal system model, where the received signal 𝐫 is constructed of three additive parts: the clutter 𝐫𝑒, the target signal 𝐫𝑡, and the noise 𝐞.

In the Novelda transceiver, each measurement consists of 128 samples. The received signal 𝐫 and its subcomponents 𝐫_𝑒, 𝐫_𝑡, and 𝐞 are therefore expressed by 128-dimensional vectors as 𝐫=𝐫_𝑒+𝐫_𝑡+𝐞, (1) where 𝐫=[𝑟(1),𝑟(2),…,𝑟(128)]^𝑇, (2) with 𝑇 transposition, and the same for 𝐫_𝑒, 𝐫_𝑡, and 𝐞.

Let vectors 𝐫^{𝑖−(𝑛−1)},𝐫^{𝑖−(𝑛−2)},…,𝐫^𝑖−1,𝐫^𝑖 denote the measurements at time 𝑡_{𝑖−(𝑛−1)},𝑡_{𝑖−(𝑛−2)},…,𝑡_𝑖−1,𝑡_𝑖, and denote a measurement matrix 𝐗^𝑖 at time 𝑡_𝑖 by the present and past 𝑛 measurements as 𝐗^𝑖=𝐫^{𝑖−(𝑛−1)},𝐫^{𝑖−(𝑛−2)},…,𝐫^𝑖−1,𝐫^𝑖. (3) From (1), the measurement matrix 𝐗^𝑖 can be written as 𝐗^𝑖=𝐗^𝑖_𝑒+𝐗^𝑖_𝑡+𝐄^𝑖, (4) where 𝐗^𝑖_𝑒, 𝐗^𝑖_𝑡, and 𝐄^𝑖 are referred to as the clutter matrix, the target matrix, and the noise matrix, respectively.

4. Adaptive Clutter Mapping

The Novelda transceiver has a particular property: the received signal at each measurement may be rescaled and shifted by a bias. This means that even in a static environment, the clutter map may change with the measurement. Without losing generality, each clutter measurement 𝐫^𝑖_𝑒 can be modeled by a rescaling factor 𝛼^𝑖 and a shifted bias 𝑐^𝑖 as 𝐫^𝑖_𝑒=𝛼^𝑖𝐫_𝑐+𝐜^𝑖, (5) where 𝐫_𝑐 represents the clutter vector, bias vector 𝐜^𝑖=^1⋮₁𝑐^𝑖, and 𝑐^𝑖 is a constant. Therefore, 𝐗^𝑖_𝑒=𝛼^{𝑖−(𝑛−1)}𝐫_𝑐+𝐜^{𝑖−(𝑛−1)},…,𝛼^𝑖𝐫_𝑐+𝐜^𝑖=𝛼^{𝑖−(𝑛−1)}𝐫_𝑐,…,𝛼^𝑖𝐫_𝑐+𝐜^{𝑖−(𝑛−1)},…,𝐜^𝑖=𝐑^𝑖_𝑐+𝐂^𝑖, (6) where 𝐑^𝑖_𝑐=𝛼^{𝑖−(𝑛−1)},…,𝛼^𝑖𝐫_𝑐,𝐂^𝑖=𝐜^{𝑖−(𝑛−1)},…,𝐜^𝑖. (7) The maximum rank of the clutter matrix 𝐗^𝑖_𝑒 is 2 by the assumption that there are only two independent vectors [𝛼^{𝑖−(𝑛−1)},…,𝛼^𝑖] and [𝐜^{𝑖−(𝑛−1)},…,𝐜^𝑖].

Due to the property of (6), the method of singular value decomposition (SVD) [16] is used for the clutter mapping, referred to as the SVD clutter mapping.

Suppose that 𝐗^𝑖 is an 𝑚×𝑛 matrix with a rank of 𝐿. Then, it can be factorized as 𝐗^𝑖=𝐔Σ^𝑖𝐕^𝐻𝜎=𝐔^𝑖10⋯⋯⋯0⋮⋱0⋯⋯00⋯⋱0⋯000⋯𝜎^𝑖_𝐿⋯0𝐕0⋯⋯⋯00⋮⋮⋮⋮⋮⋮00⋯⋯⋯0^𝐻, (8) where 𝐔 is an 𝑚×𝑚 unitary matrix, Σ^𝑖 is an 𝑚×𝑛 matrix with diagonal entries of nonnegative-real numbers, and 𝐕 is an 𝑛×𝑛 unitary matrix. The nonzero entries 𝜎^𝑖_𝑙,𝑙=1,…,𝐿, the singular values of matrix 𝐗^𝑖, are sorted in the descending order. Thus, 𝐗^𝑖 can be expressed by a summation of 𝐿 one-rank matrices: 𝐗^𝑖=^𝐿_𝑙=1𝐮_𝑙𝜎^𝑖_𝑙𝐯^𝐻_𝑙, (9) where 𝐮_𝑙, 𝐯^𝐻_𝑙, and 𝜎^𝑖_𝑙 are the left eigenvector, the right eigenvector, and the singular value of 𝐗^𝑖, respectively.

According to (6), the two strongest singular values represent the clutter map, that is, 𝐫̂^𝑖_𝑒=²_𝑙=1𝐮_𝑙𝜎^𝑖_𝑙𝐯^𝑖_𝑙^𝐻, (10) where 𝐫̂^𝑖_𝑒 is defined as in (5) for the 𝑖th measurement 𝐫^𝑖. Therefore, the clutter map can be removed from the measurement 𝐫^𝑖, and the reflection of the target can be estimated accordingly: 𝐫̂^𝑖_𝑡=𝐫^𝑖−𝐫̂^𝑖_𝑒. (11)

An example of the clutter mapping is shown in Figure 5: 100 consecutive measurements (𝑛=100) of a moving metal plate (225×500mm²). The overlapping part of all measurements represents the clutter map 𝐫̂^𝑖_𝑒, and the pulses different from the clutter map indicate the echoes from the target 𝐫^𝑖_𝑡. It is observed from the figure that although the clutter is static, the clutter map has a certain variation (a thick belt of the overlapped curve). This is due to the rescaling factor 𝛼^𝑖 and the shifted bias 𝑐^𝑖 of the Novelda transceiver, as described in (5).

Figure 5 

100 consecutive measurements of a moving metal plate (225×500mm2) in the presence of clutter. The overlapping part represents the clutter map 𝐫̂𝑖𝑒, and the pulses different from the overlapping part indicate the target signals 𝐫𝑖𝑡.

The choice of the number of measurements 𝑛 in the measurement matrix 𝐗^𝑖 for the clutter mapping depends on the applications. If 𝑛 is large, the clutter map is more related to the static environment with a higher computational complexity. If 𝑛 is small, any object that appears to be static for a short time or moves slowly will be considered as a part of the clutter, with a shorter computation time.

As a reference, the direct mean method is also used for the clutter mapping (referred to as the direct-mean method) for each measurement, which is defined as 𝐫̂^𝑖_𝑒=1𝑛^𝑖_{𝑘=𝑖−(𝑛−1)}𝐫^𝑘, (12) that is, in the direct-mean method, the clutter map 𝐫̂^𝑖_𝑒 is obtained by averaging the measurements 𝐫^𝑘, 𝑘=𝑖−𝑛+1,𝑖−𝑛+2,…,𝑖, over the 𝑛 measurements. It is obvious that this method assumes that the rescaling and the bias shifting and the reflection from a moving target are zero-mean random variables, which is not always true in reality.

5. Ranging

Three ranging techniques have been investigated. The first two are sample based, which means that the ranging is obtained by finding the sample index of the pulse front in the received target signal. The last one is a fractional-sample method, in order to get a higher ranging resolution.

5.1. Pulse Signature Matching

The received pulse signature 𝐫_𝑠 is defined first in this method.

In order to maintain the robustness over a range of objects, the pulse signature is obtained by (1) averaging target pulse signals over 20 different targets, measured in anechoic chamber; (2) using a rectangular window (referred to as the pulse signature window) to select the main lobes of the averaged pulse signal 𝐫_avg as the pulse signature.

Referring to Figure 6, we define 𝑟_𝑠(𝑚)=𝑟0,𝑚<0,_avg𝑛_st+𝑚,0≤𝑚≤𝑁_𝑤,0,𝑚>𝑁_𝑤, (13) where 𝑛_st and 𝑁_𝑤 are the starting sample index and the sample width of the pulse signature window in the averaged signal, respectively.

Figure 6 

The pulse signature is defined by the main lobes (inside the pulse signature window) of the averaged target signals over 20 different targets measured in anechoic chamber. The same window sliding through the target signal sequence is used for STFT in pulse-spectrum signature matching.

Then, the cross-correlation 𝑋_corr(𝑘) between the target signal 𝐫_𝑡 and the pulse signature 𝐫_𝑠 with different time delay 𝑘 is calculated by 𝑋_corr(𝑘)=¹²⁸_𝑚=1𝑟_𝑡(𝑚)𝑟_𝑠(𝑚−𝑘),1≤𝑘≤128−𝑁_𝑤. (14) The peak value of 𝑋_corr(𝑘) appears obviously at such a time delay 𝑘 that the pulse front of the pulse signature coincides with the pulse front of the target signal. Therefore, the sample index of the target pulse front 𝑗̂, referred to as the target index, is estimated by 𝑗̂=argmax_{𝑘∈(1,128−𝑁𝑤)}𝑋_corr(𝑘). (15)

In a traditional radar problem, this classic approach is referred to as matched filter [17, 18]. It has its advantage of simplicity. However, the similarity between the target signal pulse and the pulse signature plays an important role for the accuracy of this method. In far-field applications, the distance between the radar system and the target is much larger than that of the pulse duration. Namely, the time it takes for the reflected signal coming back to the receiver is much longer than the signal duration itself, and thus the changing of the pulse shape can be ignored in this case [19]. However, in short-range indoor applications, the shape difference between the target signal and the pulse signature will cause measurement errors. In addition, multiple reflections between the target and the clutter may happen and lead to complicated scenario. For example, the ringing part can be very similar to the main portion of the pulse, in both the shape and the amplitude. A big ranging error may arise in such cases.

5.2. Pulse-Spectrum Signature Matching

The UWB signal spans a large spectrum, and it has been observed in this work that the spectrum of the Novelda transceiver together with the SGBT antennas within a main bandwidth (such as 2–4 GHz in this work) does not severely vary with different scenarios. In other words, the changes of the spectrum with respect to different targets and environments appear mainly in a few frequencies, while the change of the pulse shape in time domain can be significant. It is then natural to take advantage of this spectrum-stable property, instead of using only time-domain analysis. A joint time-frequency domain ranging method, the pulse-spectrum signature matching, is therefore introduced, as follows.

(1)  The Short-Time Fourier Transform (STFT) [20, 21], is applied in the first step to the target signal as 𝑅_𝑡(𝑘,𝜔)=^𝑁𝑤−1_𝑚=0𝑟_𝑡(𝑚+𝑘)𝑊(𝑚)𝑒^{−𝑗𝜔𝑚}, (16) where 𝑟_𝑡(𝑚) is the sample of the target signal, 𝑊(𝑚) is the sample of a window function 𝑊, angular frequency 𝜔=0,2𝜋(1/𝑁_𝑤),…,2𝜋((𝑁_𝑤−1)/𝑁_𝑤), and 𝑗, imaginary unit. In this work, a rectangular window function (referred to as the STFT window), with the same width of 𝑁_𝑤 as that of the pulse signature window, is used; see Figure 6.

Within each window, the short-time Fourier transform in (16) can be rewritten by the multiplication of the target signal 𝐫_𝑡 with an 𝑁_𝑤-point discrete Fourier transform (DFT) matrix 𝐖 [22, 23] defined as 1𝐖=𝑁_𝑤11⋯11𝑒^{−𝑗(2𝜋/𝑁𝑤)}⋯𝑒^{−𝑗(2(𝑁𝑤−1)𝜋/𝑁𝑤)}1𝑒^{−𝑗(4𝜋/𝑁𝑤)}⋯𝑒^{−𝑗(4(𝑁𝑤−1)𝜋/𝑁𝑤)}1𝑒^{−𝑗(6𝜋/𝑁𝑤)}⋯𝑒^{−𝑗(6(𝑁𝑤−1)𝜋/𝑁𝑤)}⋮⋮⋮1𝑒^{−𝑗(2(𝑁𝑤−1)𝜋/𝑁𝑤)}⋯𝑒^{−𝑗(2(𝑁𝑤−1)2𝜋/𝑁𝑤)}. (17)

In general, we can define the STFT window function by 𝐖^′=diag(𝜷)𝐖, (18) where 𝜷=(𝛽₁,𝛽₂,…,𝛽_𝑁𝑤) is a coefficient vector whose entry gives a weight to each frequency component, which can also be referred to as the spectrum signature window, shown in Figure 7. In this work, we set 𝛽_𝑖=1 for all the frequencies in the band of 2–4 GHz, and 𝛽_𝑖=0 for the frequencies out of the band.

Figure 7 

The different spectrums when the STFT window slides through the target signal. The solid black line presents the spectrum signature.

(2) The Spectrum Signature is defined in the second step. Applying the same STFT window as the pulse signature window (the same width and sample index location) to the pulse signature 𝐫_𝑠, the spectrum of the pulse signature can be obtained by 𝐖^′𝐫_𝑠, shown in Figure 7. Here by using 𝐖^′, we use only the main part of the whole spectrum inside the spectrum signature window as the spectrum signature, for simplicity and robustness of the algorithm, such as the band from 2 to 4 GHz.

(3) The Spectrum Signature Matching is then introduced. The cross-correlation 𝑋_corr𝑓(𝑘) is calculated, similar to the pulse signature matching method, by 𝑋_corr𝑓(𝑘)=𝐖^′𝐫_𝑡𝑘∶𝑘+𝑁_𝑤^∗⋅𝐖^′𝐫_𝑠, (19) with 𝑘∈(1,128−𝑁_𝑤), and the superscript ∗ conjugation. A clearly high correlation can be found between the spectrum signature and the spectrum of the target signal when the STFT window is located at such a position that the main lobes of the target signal are inside it, while for the other positions, the correlation is low. The target index 𝑗̂ can therefore be estimated by 𝑗̂=argmax_{𝑘∈(1,128−𝑁𝑤)}𝑋_corr𝑓(𝑘). (20)

(4)  The Pulse and the Spectrum Signature Matchings are combined in the final step, in order to achieve a more accurate and robust ranging algorithm. A linear combination of the pulse and the spectrum signature matchings is applied as 𝑋_{com_corr}(𝑘)=1−𝛾𝑋^′_corr(𝑘)+𝛾𝑋^′_corr𝑓(𝑘), (21) with 𝑘∈(1,128−𝑁_𝑤), where the normalized cross-correlations in time and frequency domains are defined by 𝑋^′_corr(𝑘)=𝑋_corr(𝑘)max𝑋_corr(𝑘),𝑋^′_corr𝑓(𝑘)=𝑋_corr𝑓(𝑘)max𝑋_corr𝑓(𝑘), (22) where max(𝑥) is the maximum value of the vector 𝑥.

The final estimated target index 𝑗̂ (therefore the ranging) is found by 𝑗̂=argmax_{𝑘∈(1,128−𝑁𝑤)}𝑋_{com_corr}(𝑘). (23) The empirical constant 𝛾=0.7 is determined by cross-validation.

5.3. Subsample Delay Estimation

The above-discussed ranging approaches are sample based. The best achievable ranging resolution is therefore half the sample resolution: 4.167/2=2.08mm (refer to Section 2). In order to obtain a better ranging resolution, the fractional sample shift has to be determined. An estimation of subsample delay based on the Fourier transform is introduced as follows.

Suppose that the target sample index 𝑗̂ has been obtained by the pulse-spectrum method, and actually the target pulse front of 𝑟_𝑡 is located at sample 𝑗̂+𝑗_𝜏̂, where |𝑗_𝜏̂|<1 is a fractional sample delay around 𝑗̂. Then, we locate the pulse signature 𝑟_𝑠 at sample 𝑗̂ and let 𝑟_𝑡main=𝑟_𝑡(𝑗̂∶𝑗̂+𝑁_𝑤−1) be the main portion of the received pulse. Since 𝑟_𝑡main and 𝑟_𝑠 have a slight difference in both shape and phase, applying the Fourier transform, we get the following relation for the transformed signals 𝑅_𝑡(𝜔) and 𝑅_𝑠(𝜔) [24]: 𝑅_𝑡(𝜔)𝑅_𝑠(𝜔)=𝐶(𝜔)𝑒^{−𝑗𝜔𝜏}, (24) where 𝜏 is the time delay between 𝑟_𝑡main and 𝑟_𝑠, and 𝐶(𝜔) results from the shape difference.

However, since the main portion of the signal does not change significantly and therefore the pulse signature 𝑟_𝑠 is assumed to be identical in shape compared to 𝑟_𝑡main, namely, we have 𝑟_𝑡main(𝑡)≈𝑟_𝑠(𝑡−𝜏), (25) and thus, 𝑅_𝑡(𝜔)𝑅_𝑠(𝜔)≈𝑒^{−𝑗𝜔𝜏}. (26) By retrieving the angle, we have ∠𝑅_𝑡(𝜔)𝑅_𝑠(𝜔)≈−𝜔𝜏. (27)

The above expression is valid for all frequency points within the bandwidth defined by the spectrum signature window, that is, 𝜔∈𝜔_𝑙,𝜔_𝑙+1,…,𝜔_ℎ=2𝜋𝑓_𝑙,𝑓_𝑙+1,…,𝑓_ℎ, (28) where 𝑓_𝑙 and 𝑓_ℎ are the frequency points of the low and the high ends of the bandwidth, respectively. For example, 𝑓_𝑙=2GHz and 𝑓_ℎ=4GHz in this work, shown in Figure 7. Therefore, the slight time shift 𝜏 can be retrieved from the above linear relation by least squares estimation.

6. Adaptive Calibration by Direct Coupling

Adaptive calibration is a necessity for this UWB radar system due to the following. (1) The heating of the radar device due to a long time continuous usage may cause different time delays of the transmitted pulses. (2) The cables connecting the transceiver to the antennas may have different conditions, such as the length and the bending shape, which also cause different time delays. An adaptive calibration by using the direct coupling between the two antennas is therefore introduced.

The estimated distance 𝑑^𝑖_𝑡 between the target and the antenna center at the 𝑖th measurement can be expressed by 𝑑^𝑖_𝑡=Δ_𝜏𝑗^𝑖_𝑡̂−𝑗̂^𝑖_𝑎𝑡2, (29) where Δ_𝜏 is the ranging resolution between two consecutive samples, 𝑗^𝑖_𝑡̂ is the target index, and 𝑗̂^𝑖_𝑎𝑡 is the antenna-center pulse sample index (the pulse sample index when the pulse front is passing the Tx antenna center). Here, Δ_𝜏 and 𝑗^𝑖_𝑡̂ are known, but 𝑗̂^𝑖_𝑎𝑡 is unknown.

Now, we utilize the direct coupling between the two antennas to eliminate the unknown parameter 𝑗̂^𝑖_𝑎𝑡.

Assume that the distance between the two antennas 𝑑_ant is smaller than that between the antennas and the target. Since 𝑑_ant is fixed and known, and the direct coupling is the first received pulse, 𝑑_ant can be expressed by 𝑑_ant=Δ_𝜏𝑗̂^𝑖_𝑎𝑟−𝑗̂^𝑖_𝑎𝑡, (30) where 𝑗̂^𝑖_𝑎𝑟 is the sample index of the first received pulse determined by using the ranging methods discussed in the previous section. Therefore, from (29) and (30), we have 𝑑^𝑖_𝑡=Δ_𝜏𝑗^𝑖_𝑡̂−𝑗̂^𝑖_𝑎𝑟+𝑑_ant2. (31)

7. Tracking

A Kalman filter [25] tracking algorithm is applied in this work, and the pulse-spectrum signature matching with the subsample delay estimation is used as the measurements in the tracking algorithm.

7.1. One-Dimensional Tracking

The state 𝐱_𝑘 is constructed of the position 𝑝_𝑘 and the velocity 𝑝_𝑘̇ as 𝐱_𝑘=𝑝_𝑘,𝑝̇_𝑘^𝑇. (32) The measurement 𝐳_𝑘 is obtained by using the ranging method presented in Section 5. Then the state 𝐱_𝑘 is adaptively corrected in real time by the Kalman filter update.

7.2. Two-Dimensional Tracking

For two-dimensional space, a pair of the UWB radar systems are set up orthogonally, as shown in Figure 8, for the two-dimensional tracking. The coordinate (𝑥,𝑦) of the target position can be calculated by 𝑥=𝑑_𝑥−𝑟_𝑥cos𝜃_𝑥,𝑦=𝑑_𝑦−𝑟_𝑦cos𝜃_𝑦, (33) where 𝑟_𝑥 and 𝑟_𝑦 are the ranging values measured by UWB radars 𝑋 and Y, with their positions at (𝑑_𝑥,0) and (0,𝑑_𝑦), respectively, and 𝜃_𝑥𝑑=arccos²+𝑟²_𝑥−𝑟²_𝑦2𝑑𝑟_𝑥𝑑+arccos^𝑥𝑑,𝜃_𝑦𝑑=arccos²+𝑟²_𝑦−𝑟²_𝑥2𝑑𝑟_𝑦𝑑+arccos^𝑦𝑑,𝑑²=𝑑²_𝑥+𝑑²_𝑦. (34)

Figure 8 

Setup for 2-dimensional tracking.

By extending the Kalman filter to the two-dimensional case, the state and the observation vectors are defined as 𝐱^′_𝑘=𝑥_𝑘,𝑥̇_𝑘,𝑦_𝑘,𝑦̇_𝑘^𝑇,𝐳^′_𝑘=𝑧_𝑥𝑘,𝑧_𝑦𝑘^𝑇, (35) where 𝑧_𝑥𝑘 and 𝑧_𝑦𝑘 are the observation values of 𝑥 and 𝑦 coordinates, respectively, calculated based on the measurements 𝑟_𝑥 and 𝑟_𝑦 by (33).

More details about Kalman filter equations and update can be found in [25, 26].

8. Measurement Evaluations

All algorithms developed for clutter mapping, ranging, and tracking are evaluated by the measurements.

8.1. Clutter Mapping

Both the SVD and the direct-mean methods have been evaluated in an indoor environment with and without moving targets.

Without moving targets, the measurement consists of only the clutter and noise: 𝐫^𝑖₀=𝐫^𝑖_𝑒+𝐞^𝑖, and we assume that the noise level is much lower than that of the clutter. Keep in mind that in spite of the static clutter, the received signal 𝐫^𝑖₀ varies a bit due to the rescaling factor and the shifted bias.

The evaluation is carried out in terms of the root-mean-square error (RMSE) between the clutter map 𝐫̂^𝑖_𝑒 (Section 4) and the real measurement 𝐫^𝑖₀, which can be expressed by RMSE_Clutter=1𝑛^𝑛_𝑖=1𝐫̂^𝑖_𝑒−𝐫^𝑖₀². (36)

Figure 9 shows the RMSE_Clutter values. From the figure, it can be observed that the SVD clutter mapping gives smaller RMSE values than the direct-mean method does, stating the superiority of the SVD clutter mapping.

Figure 9 

RMSE values for evaluation of the SVD and the direct-mean method without moving target.

With the presence of moving targets in the clutter, the evaluation becomes complicated since both the clutter map and the target signal vary. An off-line emulating method is therefore introduced for the evaluation of this case as follows.

A moving target is measured in the anechoic chamber at our lab as 𝐫^𝑖_𝑡, where it is assumed no clutter. Then, the measured data 𝐫^𝑖_𝑡 is added to the previous measurements of the clutter without moving targets 𝐫^𝑖₀=𝐫^𝑖_𝑒+𝐞^𝑖 to emulate the scenario of the target in the clutter by 𝐫^𝑖=𝐫^𝑖_𝑡+𝐫^𝑖_𝑒+𝐞^𝑖. (37)

The emulated measurements are shown in Figure 10, where 𝑛=100 in the measurement matrix 𝐗. The RMSE of the clutter map 𝐫̂^𝑖_𝑒 (by both the SVD and direct-mean clutter mappings) with respect to the clutter 𝐫^𝑖₀ is defined in the same way as (36). The data are shown in Figure 11, illustrating again the superiority of the SVD clutter mapping.

Figure 10 

Emulated measurement matrix by adding the measurements of a moving target in anechoic chamber to the measurements of a clutter without moving targets.

Figure 11 

RMSE values for evaluation of the SVD and the direct-mean clutter mappings with a moving target.

8.2. Ranging

The off-line emulating method is used for the evaluation of our ranging algorithms, since we do not have any other means to obtain the accurate ranging values of a moving object than manually measuring a static object by a ruler.

A clutter, an indoor environment without moving targets, is measured first. The measurement matrix 𝐗⁰ can be expressed by 𝐗⁰=𝐫⁻⁹⁹,𝐫⁻⁹⁸,…,𝐫⁻¹,𝐫⁰, (38) where we use 𝑛=100 measurements for 𝐗⁰, and the superscript 𝑖≤0 represents the clutter without targets. Using the SVD clutter mapping, the clutter map 𝐫̂⁰_𝑒 can be obtained.

Then, a metal ball with a diameter of 20 mm is placed into the environment at 50 different locations statically. At each location, one measurement is performed, referred to as 𝐫^𝑖,𝑖=1,2,…,50.

The emulating measurement matrix 𝐗^𝑖 for the “moving” metal ball is created off line by 𝐗^𝑖=𝐫^−99+𝑖,𝐫^−98+𝑖,…,𝐫^−1+𝑖,𝐫^𝑖, (39) where 𝑖=1,2,…,50. For each 𝐗^𝑖, the adaptive clutter map 𝐫̂^𝑖_𝑒 can be obtained, and the target signal 𝐫̂^𝑖_𝑡 is acquired by the clutter map removal shown in (11). The ranging algorithms are then applied to the target signal 𝐫̂^𝑖_𝑡 to have the ranging values for these 50 locations.

Figure 12 shows the ranging results over the 50 locations. As a comparison, the ranging without the clutter map removal is presented in Figure 13. Table 1 summarizes the RMSEs for the different ranging algorithms over the 50 locations. The true ranging values of the target are measured by a ruler with 1 mm resolution.

Table 1 

Comparison of the ranging techniques in terms of RMSE between the real and estimated position for each measurement 𝑖.

Figure 12 

Evaluation of the ranging algorithms with the clutter map removal.

Figure 13 

Evaluation of the ranging algorithms without the clutter map removal.

The ranging evaluation starts from 250 mm, which is longer than the distance of 200 mm between the Tx and the Rx antennas, as explained in Section 2.2.

From Figure 12 and Table 1, it can be observed that the pulse-spectrum signature matching together with the subsample delay estimation gives the best result, achieving a resolution of 1.4 mm, while the pulse signature matching provides much less accurate ranging. Furthermore, it can be concluded that the clutter map removal is very critical for the ranging accuracy.

The pulse-spectrum signature matching is a fast algorithm, since it involves only linear transformations (STFT) and calculations of cross-correlation. This makes the algorithm a very useful one in real-time applications, such as tracking.

8.3. Tracking

It is difficult for us to evaluate the tracking algorithm quantitatively, since the off-line method is not valid for real-time applications, and we do not have other means to get the accurate values of the position and the velocity of a moving target. The qualitative evaluation is accordingly applied.

Figure 14 shows the setup for one-dimensional tracking evaluation, by using the UWB radar as a see-through-wall radar. The Tx and the Rx SGBT antennas are located on one side of a 515 mm thick concrete corridor wall. The target, a 213 mm-in-diameter metal cookie jar, is moved back and forth manually behind and orthogonally to the wall.

Figure 14 

The setup for the evaluation of the one-dimensional tracking algorithm through a concrete corridor wall.

Figures 15 and 16 show the estimated positions and velocities by using the tracking algorithm. It can be seen that the Kalman filter algorithm gives a very reasonable result: the moving target has slightly different period and amplitude of the back and forth “oscillation” of both the position and the velocity, which agrees well with the movement by a human hand.

Figure 15 

The estimated position by the Kalman filter tracking algorithm for the one-dimensional see-through-wall evaluation.

Figure 16 

The estimated velocity by the Kalman filter tracking algorithm for the one-dimensional see-through-wall evaluation.

It should be noted that if the target is not moving orthogonally to the wall, the one-dimensional tracking algorithm with one radar device can only discriminate the distance between the target and the antennas, but not the positions.

The two-dimensional tracking algorithm is evaluated also qualitatively. Figure 17 (left) shows the setup: two UWB radars are located orthogonally, and a metal object is moved manually. The graphic user interface implemented in Matlab shows the target with a red dot. We have observed that the red dot follows the movement of the target very well. An image printed from the screen is shown in Figure 17 (right) for the illustration purpose.

Figure 17 

An orthogonal measurement setup for two-dimensional tracking (left) and the graphical user interface implemented in Matlab (right), where the red dot indicates the location of the target.

9. Conclusion

A compact UWB radar system for indoor and through-wall ranging and tracking of moving objects has been built up by using the compact self-grounded Bow-Tie antennas and the low-cost Novelda transceiver. Robust and accurate algorithms for ranging and tracking have been developed. The evaluation by measurements shows that the ranging resolution of this UWB system has achieved to 1.4mm RMS accuracy, and a fast and real-time tracking solution for through wall is obtained.

The main contribution of this UWB radar system is that the system itself has commercially low-cost, compact size. Equipped with the ranging algorithms, it provides a very high time domain resolution. Furthermore, the penetration ability together with the high resolution and the compact system size gives possibilities for many applications. However, the trade-off here is that due to the small size of the system, the dynamic range is relatively low, and therefore, it can only be used for short-range applications.

Acknowledgments

This work is a collaboration between Imego AB (The Institute of Micro and Nanotechnology in Gothenburg) and Chalmers University of Technology. The authors would like to thank Kenneth Kalmstrom, Lars Landen, Peter Bjorkholm, Jakob Blomgren, Dimitar Kolev, and Sohaib Maalik for their help in the project. This work has been supported in part by The Swedish Foundation for Strategic Research (SSF) within the Strategic Research Center Charmant.