Human Target Tracking in Multistatic Ultra-Wideband Radar

Multistatic Ultra-Wideband

Radar

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op Woensdag 16 April 2014 om 12:30 uur

door

Yuan HE

Master of Science, National University of Defense Technology, Changsha, China

Prof. DSc. A. G. Yarovoy

Samenstelling promotiecommissie:

Rector Magnificus, voorzitter

Prof. ir. F. Le Chevalier, Technische Universiteit Delft, promotor Prof. DSc. A. G. Yarovoy, Technische Universiteit Delft, promotor Prof. dr.ir. G.J.T. Leus, Technische Universiteit Delft

Prof. ir. P. van Genderen, Technische Universiteit Delft Prof. ir. P. Hoogeboom, Technische Universiteit Delft Prof. Ing. D. Kocur, Technical University of Kosice

Prof. dr.ir. H.W.J. Russchenberg, Technische Universiteit Delft, reservelid

Human Target Tracking in Multistatic Ultra-Wideband Radar PhD Thesis at Delft University of Technology

Copyright c 2014 by Y. HE

ISBN 978-94-6259-157-8

All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.

An electronic version of this dissertation is available at

http://repository.tudelft.nl/.

Multistatic Ultra-Wideband

Radar

Yuan HE

Group of Microwave Sensing, Signals and Systems (MS3)

Faculty of Electrical Engineering, Mathematics and Computer Science

Delft University of Technology

A thesis submitted for the degree of

Doctor of Philosophy

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Multistatic ultra-wideband radar is acknowledged to be useful for hu-man tracking in indoor surveillance. In this thesis, a global signal processing chain for detection and localization of moving human targets using a multi-static ultra-wideband radar system is proposed. The design options for the system are presented, and the required signal processing steps are summa-rized. They include critical steps of tracking, target feature extraction,and target association.

A decentralized tracking approach is designed for indoor surveillance. The 1D bistatic tracking rejects clutter and multipath significantly. The 2D tracker employs the global nearest neighbor based Kalman filter to track targets in Cartesian coordinates. The algorithm performance is compared with that of the conventional centralized approach on both simulated and experimental data, and shown to improve clutter and multipath rejection. For feature extraction, the advantages of using range-Doppler imaging are analyzed. The hypothesis-testing based and the Keystone transform based range migration compensation approaches are extended to eliminate the Doppler widening effect in range-Doppler processing of ultra-wideband radar. The proposed compensation approaches are applied to both simu-lated and experimental data.

Special attention is then devoted to the critical issue of target associa-tion. Two slow-time features, video time density function and self-similarity matrix, are proposed to characterize target responses in different receiving channels. They indicate the slow-time evolution characteristic of the mov-ing human targets, while staymov-ing invariant to radar observation angles. Auction algorithm based global nearest neighbor approach is proposed to tackle the measurement-to-measurement association issue. The decentral-ized tracking approach is experimentally verified on measured data for in-door detection and location of multiple human targets.

Finally, as a by-product of human target feature analysis, range-Doppler surface is proposed to demonstrate the overall target information in range-Doppler video sequence.

The conclusion emphasizes the main achievements and highlights the novelties. The recommendation for future work is presented finally to point out potential research topics related to this thesis.

Keywords: Multistatic UWB radar, indoor surveillance, human target tracking, target association, range-Doppler imaging.

Abstract v Contents vii List of Figures 1 List of Tables 5 Glossary 9 1 Introduction 11

1.1 Multistatic UWB radar design for indoor surveillance . . . 11

1.2 Target tracking in multistatic radar . . . 13

1.3 Decentralized tracking . . . 16

1.3.1 Signal pre-processing . . . 16

1.3.2 Target detection . . . 16

1.3.3 Target positioning . . . 17

1.3.4 Target tracking . . . 18

1.4 Challenges and chosen approaches . . . 20

1.5 Contributions and outline . . . 22

2 Decentralized tracking in multistatic UWB Radar 25 2.1 Decentralized tracking framework . . . 25

2.1.1 Target detection . . . 27

2.1.2 1D bistatic tracking . . . 29

2.1.3 Trilateration . . . 31

2.1.4 2D Cartesian tracking . . . 35

2.2 Decentralized tracking result . . . 36

3 Range-Doppler processing in multistatic UWB radar 43

3.1 Introduction . . . 43

3.1.1 Motivation . . . 43

3.1.2 Human radar backscattering model . . . 45

3.1.3 Range-Doppler processing in narrow-band radar . . 47

3.1.4 Challenges of range-Doppler processing in UWB radar 50 3.2 Hypothesis testing based migration compensation . . . 53

3.2.1 Hypothesis-testing based approach . . . 53

3.2.2 Processing result . . . 57

3.2.3 Conclusion . . . 59

3.3 Keystone transform based migration compensation . . . 60

3.3.1 Keystone transform based approach . . . 60

3.3.2 Processing result . . . 62

3.3.3 Conclusion . . . 64

3.4 Range-Doppler video sequence . . . 68

3.5 Conclusion . . . 70

4 Video time density function based slow-time feature 73 4.1 Critical issue: target association in decentralized approach . 73 4.2 Slow-time feature: video time density function . . . 75

4.3 Video time density function of simulated human target . . . 79

4.4 Experimental validation . . . 79

4.4.1 Measurement set-up . . . 79

4.4.2 Sliding window correlation of video time density func-tions . . . 81

4.5 Conclusion . . . 84

5 Self-similarity matrix based slow-time feature 87 5.1 Introduction . . . 87

5.2 Self-similarity matrix of human echo . . . 88

5.2.1 Mutual information based self-similarity matrix . . . 88

5.2.2 Extracting self-similarity matrix from radar signals . 90 5.2.3 Angle-invariant characteristic of self-similarity matrix 92 5.2.4 Gait periodicity analysis using self-similarity matrix 96 5.3 Self-similarity matrix of measured human echo . . . 96

6.2 Global nearest neighbor based association . . . 100

6.2.1 Auction algorithm . . . 101

6.2.2 Averaged association probability . . . 102

6.3 Experimental validation . . . 103

6.4 Conclusion . . . 105

7 Range-Doppler surface: a future tool to analyze target 109 7.1 Introduction . . . 109

7.2 Ellipsoid-based human backscattering model . . . 110

7.3 Range-Doppler surface (RDS) . . . 113

7.3.1 Range-Doppler surface construction . . . 113

7.3.2 High-resolution range profiles, micro-Doppler image and range-Doppler surface . . . 116

7.4 Range-Doppler surface of measured human backscattering . 118 7.5 Conclusion . . . 122

8 Conclusion and future work 125 8.1 Results and novelties . . . 125

8.2 Recommendations for future work . . . 127

Appendices 131 A Description of the radar equipment 133 A.1 M-sequence multistatic UWB radar . . . 133

A.2 Characterization of horn antennas . . . 134

A.3 Radar measurements with horn antennas . . . 135

B Range-Doppler processing in UWB radar 139 B.1 Spectrum of the migrating target . . . 139

B.2 Range-Doppler processing with range migration compensation140 B.3 Spectrum of the range compensated target . . . 141

B.4 Signal in fast-frequency/Doppler domain . . . 141

C Target trilateration using direct calculation method 143 C.1 Proof of Eq. (2.7) . . . 143

C.2 Target trilateration in specific radar set-up . . . 143

Samenvatting 157

Acknowledgements 159

Curriculum vitae 161

1.1 Human target localization in multistatic radar . . . 12

1.2 Tracking approaches in multistatic radar . . . 15

1.3 Multistatic radar with one Tx and two Rx . . . 17

1.4 Target tracking flow chart . . . 19

1.5 Measurement-to-track association classification . . . 20

2.1 Decentralized tracking scheme for multistatic UWB radar . 26 2.2 Pre-processing and detection . . . 27

2.3 Measured signals of a moving human target at different time 27 2.4 Constant false alarm rate detection scheme . . . 28

2.5 1D bistatic tracking scheme . . . 30

2.6 Simulated 1D bistatic tracking . . . 31

2.7 Trajectory comparison between centralized/ decentralized method . . . 38

2.8 Error comparison between centralized /decentralized algorithm 39 2.9 Hardware setup and the pre-processed signal . . . 39

2.10 Detection result in receiver one . . . 40

2.11 Tracking results . . . 42

3.1 The classification of radar systems with respect to range/Doppler resolution . . . 44

3.2 Thalmann model . . . 45

3.3 Motion capture markers on the human body . . . 46

3.4 One complete human gait . . . 46

3.5 Simulated UWB sinc pulse (Bw = 0.8GHz) . . . 51

3.6 Fourier transform based range-Doppler processing for a sin-gle target (CPI=0.5s) . . . 52

3.10 Measurement . . . 59

3.11 Hypothesis-testing based range-Doppler processing for mea-sured data . . . 60

3.12 Keystone Transform result . . . 65

3.13 Keystone transform result for simulated human target . . . 66

3.14 Range-Doppler processing result for human target . . . 67

3.15 Radar signals of the simulated human target . . . 68

3.16 The range-Doppler images of the simulated human gait . . 69

3.17 Simulated range Doppler video sequence of the human target 70 4.1 Multi-target trilateration result . . . 74

4.2 Slow-time feature based association overview . . . 76

4.3 Simulated human target responses . . . 80

4.4 Correlation based comparison between simulated VTDFs . 81 4.5 Human target responses . . . 82

4.6 VTDF in two receivers of multistatic UWB radar . . . 83

4.7 Sliding window correlation result . . . 84

4.8 Cadence frequency analysis on sliding window correlation result . . . 85

5.1 Alignment of the range profiles . . . 91

5.2 Alignment of the range-Doppler video sequence . . . 91

5.3 Self-similarity matrix of radar signals . . . 92

5.4 Simulated radar locations: a person is walking from P1 to P2 93 5.5 SSM of radar signals from four radar locations . . . 94

5.6 Self-similarity matrix of different body parts . . . 95

5.7 Gait analysis based on self-similarity matrix . . . 95

5.8 Human backscattering in multistatic UWB radar . . . 97

5.9 Gait analysis for the measured human target data . . . 98

6.1 Measurement set-up . . . 103

6.2 Detection and 1D tracking results . . . 104

6.3 Range-Doppler video sequence . . . 106

6.4 Averaged association probability with different slow-time win-dow sizes . . . 107

7.3 3D trajectories of different body segments . . . 113

7.4 Simulated human backscattering . . . 114

7.5 RD images of one complete human gait . . . 115

7.6 Range-Doppler surface of simulated human target . . . 116

7.7 Comparison of range-Doppler surface projection with HRRP and micro-Doppler image . . . 118

7.8 Range-Doppler surface of different human body segments . 119 7.9 Range-Doppler surface of different human activities . . . 120

7.10 PulsOn 400 radar . . . 121

7.11 Range-Doppler surface of a real human target (threshold=-25dB) . . . 123

7.12 Range-Doppler surface of two human targets (threshold=-23dB) . . . 124

A.1 Radar electronics and signal . . . 134

A.2 Antenna measurements . . . 135

A.3 Antenna gain . . . 135

A.4 Effective antenna pattern . . . 136

A.5 Face-to-face measurement . . . 136

A.6 Backscattering from a metal plate . . . 137

3.1 Simulation parameters . . . 51

3.2 Simulation parameters . . . 63

Abbreviations

AOA Angle-of-arrival

CA-CFAR Cell-average constant false alarm rate detection CFAR Constant false alarm detection

CPI Coherent processing interval DFT Discrete Fourier Transform EKF Extended Kalman filter FFT Fast Fourier Transform FT Fourier Transform GNN Global nearest neighbor HRRP High-resolution range profile HT Hypothesis-testing

IDFT Inverse discrete Fourier Transform IFT Inverse Fourier Transform

JPDAF Joint probabilistic data association filter KF Kalman filter

MI Mutual information

MIMO Multiple-input-multiple-output MTI Moving target indication MUSIC MUltiple SIgnal Classification

NB Narrow-band

OS-CFAR Order-statistics constant false alarm rate detection PRF Pulse repetition frequency

PRI Pulse repetition interval RCS Radar cross section

RD Range-Doppler

RDS Range-Doppler surface

RDVS Range-Doppler video sequence RFI Radio frequency interference RMSE Root mean square error RSS Received signal strength SIMO Single-input-multiple-output SNR Signal to noise ratio

SSM Self-similarity matrix TDOA Time-difference-of-arrival TOA Time-of-arrival

TTW Through-the-wall UWB Ultra-wideband

∆R Radar range resolution Bd Doppler spectrum width

Bs Sub-band spectrum width

Bw Signal bandwidth

c Light speed

fc Radar center frequency

fd Doppler frequency

Fr Pulse repetition frequency(PRF)

Pd Detection probability

Pf a False alarm rate

T Pulse duration

Tr Pulse repetition interval(PRI)

Introduction

1.1

Multistatic ultra-wideband radar design for

indoor surveillance

Human target detection and tracking is acknowledged to be useful for in-door surveillance applications, such as airport security and anti-intruder [1]. A number of technologies have been developed for these applications, such as vision-based system [2], LIDAR [3] and radar [4,5]. Vision/LIDAR system has limited performance in poor visibility conditions, such as dusty or foggy environments. Radar [6, 7, 8, 9, 10] is a privileged tool in such cases, due to its resilience to adverse environmental conditions (including smoke or fire). According to the demand of indoor surveillance applications, the radar system designed should be reliable, portable and able to cover a large area. Fine range resolution and accuracy are also required to separate multiple human targets and multipath in heavy-cluttered environments.

Due to good observability and wide coverage, multistatic radar is thought to be a promising candidate for indoor surveillance [11, 12]. Such a mul-tistatic radar usually consists of one transmitting antenna and multiple receiving antennas, which are distributed in different locations of the moni-tored area. Indoor surveillance essentially requires low frequency (typically lower than 3 GHz) operational band. In indoor environments, usage of such a low-frequency directive receiving antenna to achieve target angle-of-arrival would be severely limited due to these constraints: an antenna array with angular scanning capability, typically larger than 1 meter, is normally costly and difficult to manipulate in encumbered environments.

rel-atively small, typically 10x10x10 cm3 – receiving antennas (see Fig. 1.1): this is a way to obtain direct line of sight to the different targets in encum-bered situations without too many restrictions on the antennas’ relative arrangement – provided their position be known or measured with ade-quate precision (typically higher than the range resolution).

Figure 1.1: Human target localization in multistatic radar using omni-directional transmitting/receiving antennas [13]

Ultra-wideband (UWB) signal is also proven to be useful for indoor surveillance, since high range resolution (typically less than 15cm) is desir-able for precise location of human targets, and for discrimination between several adjacent targets (and multipath). This unique feature together with the fact that UWB system is usually small and lightweight is the reason why UWB radar is of great interest for short-range human tracking [14,15]. According to the IEEE standard [16], UWB is defined by:

The signal has a fractional bandwidth greater than 25 % (DARPA) or 20 % (FCC).

The above design consideration leads to a low-frequency multistatic UWB system, providing only range-Doppler (RD) analysis (no angular directivity from individual receiving antennas, and no spatial coherence among different antennas) of the scene from multiple aspect angles. Cur-rently, some of the multistatic UWB systems [17,18] use a quasi-monostatic transmitter/receiver antenna configuration, designed particularly for through-the-wall applications. For instance, the antenna separation was set to less than 50 cm in [19]. Such quasi-monostatic configurations allow an easy track association between the sensors, since the time-of-arrivals are almost identical for the different sensors. However, the key difference between through-the-wall applications and indoor surveillance is that the latter de-mands wide coverage for a relatively large area (e.g., one building). Hence,

the multistatic radar using widely separated receiving antennas is consid-ered to be more suitable for indoor surveillance applications.

For such a multistatic UWB radar, normally the receiving antenna separation is large enough to present different measurements in different receivers for the same target. This multistatic topology has been used in some applications. In [20], an active multistatic radar with widely-separated omni-directional antennas was proposed for air surveillance. In [21], a tracking framework in passive multistatic radar was described. Al-though this type of multistatic topology has been applied in many situa-tions, it has not been considered for the indoor human tracking application yet. In comparison with the quasi-monostatic configuration, the multistatic radar using widely-separated receiving antennas certainly increases global coverage and target observability, and it also enlarges the antenna virtual aperture to theoretically have a better cross-range resolution. Therefore, in this thesis, the multistatic radar with distributed receiving antennas will be investigated.

1.2

Target tracking in multistatic radar

The tracking frameworks in multistatic radar can be classified into two categories:

• Centralized tracking (see Fig. 1.2a): During every refresh interval, target detection is performed in each receiving channel, and the detec-tions are sent to the central processing unit. The detecdetec-tions are fused by trilateration to localize target in Cartesian coordinates. Finally, association and tracking are performed in Cartesian coordinates. • Decentralized tracking (see Fig. 1.2b): The targets are detected,

as-sociated and tracked in each receiving channel. The 1D tracking performed in the range-time domain acts as a clutter filter. Then, the 1D tracks formed in each channel are associated, and trilatera-tion is performed using the tracking-filtered detectrilatera-tions. Finally, all the 1D tracks are sent to the central unit for tracking in 2D Cartesian coordinates.

For short-range human tracking, one centralized algorithm was reported in [22] for multistatic UWB radar. The target detection plots from all re-ceivers are sent to the central processor after every pulse repetition inter-val, and tracking is directly performed in 2D Cartesian coordinates. One

of the problems of applying this approach to indoor surveillance is that the measurement-to-track association in 2D Cartesian coordinates remains complex, since a considerable amount of ghost targets are created due to dense clutter and multipath in indoor environments. For multistatic pas-sive radar, decentralized approaches were presented in [21,23], where 1D tracking in each receiver is performed and followed by trilateration of the tracking-filtered detections. The results show that the decentralized track-ing approach is preferable in dense-clutter and low signal-to-noise ratio scenarios, since 1D tracking allows elimination of stationary clutter and multipath echoes prior to the association between different detections.

In general, the decentralized approach is more computationally expen-sive and time-consuming, but it provides better performance than the cen-tralized approach in terms of better clutter rejection capability and smaller tracking error. Since both approaches have their advantages, the selection of the approach depends mainly on application requirement. In indoor environments, dense clutter is considered as a great challenge for radar. Therefore, the decentralized framework is thought to be more suitable to the indoor human tracking applications, since its effective clutter rejection capability has potential to partially solve the ghost target issue commonly existing in multistatic radar.

1. In tro duction Positioning Cartesian tracking Target trajectory Signal pre-processing Target detection Raw data Signal pre-processing Target detection Raw data … … … Receiver 2 Receiver N

(a) Centralized tracking

Signal pre-processing 1D Tracking Positioning Cartesian tracking Target trajectory Raw data Signal pre-processing 1D Tracking Raw data Signal pre-processing 1D Tracking Raw data … … … Target detection Target detection Target detection … Receiver 1 Receiver 2 Receiver N (b) Decentralized tracking

Figure 1.2: Tracking approaches in multistatic radar

1.3

Decentralized tracking

In the following sub-sections, attention will be given to all steps of the decentralized tracking framework. The state-of-the-art of each processing procedure will be addressed in this section, in order to identify the main challenges to be tackled.

1.3.1 Signal pre-processing

Signal pre-processing is performed to improve the signal to clutter plus noise ratio of the raw radar data before target detection. The most im-portant step of signal pre-processing is background subtraction. The main purpose of background subtraction is to reject stationary clutter such as an-tenna crosstalk, impedance mismatch response and ambient static clutter. It allows the detection of the responses from moving targets. There are dif-ferent approaches to estimate the background, such as basic averaging [24], adaptive exponential averaging [17] and moving target detection filter [25]. The difference between these methods is mainly due to their assumptions regarding clutter properties. The chosen background subtraction approach has to find a balance between the elimination of clutter and detection of slow-moving targets.

1.3.2 Target detection

Based on statistics theory, target detection is to determine whether a target is present in the radar echo. Different statistical criteria, such as Bayes cri-terion, maximum likelihood criterion and Neyman-Pearson cricri-terion, lead to different target detectors [26]. Constant false alarm rate (CFAR) detec-tor is one of the classical detecdetec-tors. Based on Neyman-Pearson criterion, it achieves maximum detection probability Pd for a giving false alarm rate

Pf a.

There are different CFAR techniques with respect to different threshold estimation mechanisms. The selection of the CFAR detector depends on the type of the background noise and clutter in the environment. The CFAR detector designed for Gaussian distributed clutter has been investigated in [27] for UWB sensor network. For indoor target detection, it was found in [28] that exponential distribution is a typical probability distribution of indoor clutter. In [29], cell averaging CFAR, cell averaging with greatest of CFAR, order statistics CFAR and clutter-map CFAR have been compared

for their performance in indoor human detection, assuming the presence of exponential distribution clutter.

1.3.3 Target positioning

The target positioning in multistatic radar consists in estimating unknown target position based on radar measurements from all receiving channels. The whole procedure consists of two steps. The first step is to mea-sure various signal parameters, such as angle-of-arrival (AOA), received signal strength (RSS), time-of-arrival (TOA) or time-difference-of-arrival (TDOA). Then the target position is estimated using these obtained signal parameters [30]. Among all these approaches, time-based schemes (TOA and TDOA) provide better accuracy in UWB radar due to high time(range) resolution of UWB waveform. Moreover, they are less costly than the AOA-based schemes, since the latter usually requires more costly/bulky directive antennas. Although it is easier to estimate RSS than time-based methods, the range information obtained from RSS is coarser in comparison with that obtained in the time-based methods.

As a consequence, the time-based schemes are more suitable for target positioning in UWB radar. A multistatic UWB radar with one transmitter and multiple receivers is considered in this thesis. One example of the multistatic radar is shown in Fig. 1.3. Each receiver measures the target time-of-arrival, which is equivalent to target bistatic range (transmitter-target-receiver distance).

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Figure 1.3: Multistatic radar with one transmitter and two receivers

algo-rithms for target positioning. The direct calculation method [31] calculates the intersection of ellipses. In case of more than two receiving antennas, the ellipses might not intersect in one point due to noise and clutter. Then the optimization-based methods, such as the least-squares method, spherical-interpolation method and Taylor series method, are proposed to solve this problem. The target position estimation finds the solution for a set of non-linear equations, where each equation can be interpreted as an ellipse. In [31], the non-linear equations are transformed into linear equations, and the object location is estimated by the least-squares method. In spherical-interpolation method [32], the non-linear equations are linearized with an auxiliary variable, which depends on the target position. Then, the target coordinates are estimated by the least-squares method. The Taylor series method [32] linearizes the non-linear equations by expanding them using Taylor series. The set of linearized equations is solved to produce an ap-proximate location, and the process iterates until a pre-defined criterion is satisfied. The above methods differ in their performance and computa-tional complexity. The selection of the particular method depends on the required accuracy and the allowed computational complexity [30].

1.3.4 Target tracking

The goal of target tracking is to obtain trajectory using target state in-formation (e.g., range, velocity or Cartesian position). The decentralized approach includes two tracking procedures: 1D bistatic tracking and 2D Cartesian tracking. Both tracking procedures can be interpreted into four steps (see Fig.1.4):

• Measurement-to-track association: associate measurements with an existing track;

• Track smoothing: update the track with the measurements;

• Track initialization: initiate new tracks with any measurement that is not associated with the existing tracks;

• Track maintenance: delete any track that is not updated anymore.

Among the above four steps, measurement-to-track association and track smoothing are two key issues.

Measurement-to-track

association Track smoothing

Track maintenance Track initiation Target

measurements

Target tracks

Figure 1.4: Target tracking flow chart

Measurement-to-track association

Multiple target tracking techniques are fundamentally different from sin-gle target tracking techniques, because multiple target tracking algorithms need to take into account the measurement-to-track association issue. A complete summary of multiple target tracking methods can be found in [33]. The classical measurement-to-track association methods can be di-vided into two main classes (see Fig.1.5):

• Unique-neighbor association: global nearest neighbor (GNN) and multiple hypothesis tracking (MHT).

Each measurement can only be associated with one of the previously established tracks. Global nearest neighbor association computes the probability for all hypotheses in every refresh interval and chooses the best association hypothesis with the largest probability. Multiple hypothesis tracking calculates the likelihood of the measurements and the posterior probabilities of the hypothesis in a relatively long period (typically several seconds), storing only the most probable hypothesis [34].

• All-neighbor association: joint probabilistic data association filter (JPDAF).

JPDAF uses all measurements to update the existing tracks. All of the potential candidates for association to a track are combined in a single statistically most probable update, assuming that each target can only be the source of at most one measurement [35].

Measurement-to-track association

All-neighbor association Unique-neighbor association

Global nearest neighbor (GNN) association

Joint probabilistic data association filter (JPDAF) Multiple hypothesis

tracking(MHT)

Figure 1.5: Measurement-to-track association classification

Track smoothing

The most well-known track smoothing method is Kalman filter [36]. For human tracking in UWB radar, the linear Kalman filter has been applied in [37,38,39]. There are two fundamental assumptions for Kalman filter. First, the measurement equations and the target state equations are fixed. The motion model may be a linear monotonic one, but also other types of motion model can be assumed. Second, the error of the measurement equations and the error of the state equations are all zero-mean Gaussian distributed. However, if the object’s motion doesn’t concur with the as-sumed model, the accuracy degrades.

1.4

Challenges and chosen approaches

In the decentralized multistatic system, range and velocity can be mea-sured in each receiver, but the Cartesian position can only be recovered by trilateration of range measurements from multiple receivers, if the corre-sponding detections can be correctly associated from one receiver output to another. Therefore, association of the measurements obtained from dif-ferent receivers is a key issue for recovering the Cartesian position of the target. This issue is dominated by the limitations previously mentioned, and also by the multipath effect that may quite often be present in such cluttered environments.

It should be clarified that the data association addressed in this sec-tion belongs to measurement-to-measurement associasec-tion category, which has a fundamental difference with the measurement-to-track association described in Section 1.3.4. The measurement-to-track association is com-monly used in monostatic radar to assign new measurements to existing tracks. The measurement-to-measurement association usually appears in multistatic radar, and it matches the measurements from different receivers to enable trilateration afterwards.

The measurement-to-measurement association in multistatic UWB radar is a great challenge, due to the unavailability of target bearing. Similar association issue arises for multistatic passive radar due to its poor bear-ing estimation accuracy [21, 40]. In multistatic passive radar, normally all possible transmitter-target-receiver association pairs are processed by the trilateration processing without pre-selection, and the Cartesian track-ing afterwards serves partially as a tool for ghost target rejection. Some specific deghosting techniques, such as likelihood ratio testing and integer programming [21], have also been proposed to reduce the number of the ghost targets, but in general the deghosting performance is severely limited if the number of targets increases.

Facing the similar association challenge as the multistatic passive radar, multistatic UWB radar may have a different solution because the high res-olution of UWB signal enables more detailed analysis of the human target. If specific target features are extracted and used by the association algo-rithm, a better ghost target elimination capability can be expected for the multistatic UWB system. Therefore, a feature-based association approach is proposed to tackle the association issue. First, a human motion model is constructed by using motion capture data [41], and target backscattering is simulated by coherently summing UWB echoes from different scatterers. Then, simulated and experimental human backscattering is analyzed, and specific features are proposed to ’label’ target responses in each receiver. The target responses that have the same ’label’ must originate from the same target, so the correct association is found. Finally, considering mul-tiple receivers and mulmul-tiple targets, a global nearest neighbor algorithm is modified to associate the target features from different receivers.

Selecting an adequate feature is the key point of the association pro-cedure proposed above. Since the human target is observed from different aspect angles, a feature that remains the same in all receiving channels (i.e, invariant to radar observation angles), is required for association. This

feature should not be sensitive to target range/Doppler information, since they are different from one receiver to another. Representing target slow-time evolution, a slow-slow-time feature is proposed as angle-invariant, because it is not determined by precise target range or Doppler information. For example, for one human target illuminated by a multistatic UWB radar, its range and Doppler are different in multiple receivers, but its slow-time information (e.g., walking rhythm) always remains the same. In this thesis, video-time density function (VTDF, [42]) and self-similarity matrix (SSM, [43]) are extended from optical video processing to radar signal process-ing as tools to represent slow-time information of human motion. It is shown that after adequate modifications, VTDF and SSM are capable of representing human slow-time signatures.

1.5

Contributions and outline

The thesis is organized as follows:

Chapter 2: The whole signal processing chain for decentralized tracking in multistatic UWB radar is described. The detection, association and tracking techniques are presented, and experiments validating the different steps involved are provided.

The publications related to this chapter are the following:

• Y. He, T. G. Savelyev, and A. G. Yarovoy, ”Two-stage algorithm for extended target tracking by multistatic UWB radar,” in IEEE CIE International Conference on Radar, China, 2011, pp. 795–799.

• Y. He, P. Aubry, and F. Le Chevalier, ”Ultra-wideband multistatic tracking of human targets,” in IET International Radar Conference, China, 2013, pp. 1–5.

Chapter 3: The human target backscattering model is introduced. The conventional Fourier transform-based range-Doppler processing in narrow-band radar is described. Hypothesis-testing and Keystone transform based range migration compensation approaches are proposed. A new concept, range-Doppler video sequence, is proposed and compared with high-resolution range profiles and micro-Doppler images.

• Y. He, P. Aubry, F. Le Chevalier, and A. G. Yarovoy, ”Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar,” International Journal of Microwave and Wireless Technologies, 2014.

• Y. He, P. Aubry, F. Le Chevalier, and A. G. Yarovoy, ”Keystone transform based range-Doppler processing for human target in UWB radar,” in IEEE radar conference, U.S.A, 2014 (accepted).

• Y. He, F. Le Chevalier, and A. G. Yarovoy, ”Range-Doppler process-ing for indoor human trackprocess-ing by multistatic ultra-wideband radar,” in International radar symposium, Poland, 2012, pp. 250–253.

Chapter 4: The challenge of target association in multistatic UWB radar is addressed. The feature-based target association framework is presented. The slow-time feature, video time density function, is proposed to tackle the association issue. The video time density function of human target motion extracted from simulated/experimental data is demonstrated.

The publication related to this chapter is the following:

• Y. He, F. Le Chevalier, and A. G. Yarovoy, ”Association of range-Doppler video sequences in multistatic UWB radar for human track-ing,” in 9th European Radar Conference, the Netherlands, 2012, pp. 218–221.

Chapter 5: Self-similarity matrix of human backscattering in radar is investigated. The self-similarity matrices extracted from different receivers are compared using both simulated and experimental data.

The publications related to this chapter are the following:

• Y. He, P. Aubry, F. Le Chevalier, and A. G. Yarovoy, ”Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar,” International Journal of Microwave and Wireless Technologies, 2014.

• Y. He, F. Le Chevalier, and A. G. Yarovoy, ”Self-similarity analysis on human backscattering in radar,” in 10th European Radar Confer-ence, Germany, 2013, pp. 81–84.

Chapter 6: The slow-time feature based target association for decentral-ized tracking is proposed. Global nearest neighbor approach is modified

to associate slow-time features. Through-wall experiment validating the approach is provided.

The publication related to this chapter is the following:

• Y. He, P. Aubry, F. Le Chevalier, and A. G. Yarovoy, ”Decentralized tracking for human targets in multistatic ultra-wideband radar,” IET Radar, Sonar & Navigation, 2014 (Accepted with minor revision).

Chapter 7: As a by-product of the research in target feature extrac-tion, range-Doppler surface is proposed to present the target information in the range-Doppler-time space. The ellipsoid-based human backscattering model is used while constructing the range-Doppler surface. The simulated result is verified using the data of a real human target.

The results in this chapter have been submitted to:

• Y. He, P. Molchanov, T. Sakamoto, P. Aubry, F. Le Chevalier, and A. G. Yarovoy, ”Range-Doppler surface: a future tool to analyze human target in high-resolution radar,” IEEE Geoscience and Remote Sensing letter, 2014 (submitted).

Chapter 8: The conclusion is drawn, and future research direction is recommended.

Decentralized tracking for

human target in multistatic

UWB Radar

In this chapter, the signal processing chain for decentralized tracking in mul-tistatic UWB system is described. The detection, association and tracking techniques are presented, and experiments validating the different steps involved are provided.

2.1

Decentralized tracking framework

The decentralized approach implements 1D bistatic tracking in each re-ceiver separately, then fuses the tracks from all the rere-ceivers and performs the Cartesian tracking at the system level. In the time-range domain, mul-tipath and clutter are rejected, and a preliminary measurement-to-track association is performed. The overall decentralized architecture involves the following steps (see Fig.2.1):

• Target detection in each receiver;

• 1D tracking in each receiver;

• Trilateration based on detection plots in all receivers;

Figure 2.1: Decentralized tracking scheme for multistatic UWB radar

Firstly, radar raw data are pre-processed to reject the static clutter. Constant false alarm rate detection is applied to automatically detect tar-get presence. Secondly, 1D tracking employs 1D global nearest neighbor based tracker to track the targets according to their bistatic ranges, and provides the by-product of false alarm rejection and primary target associ-ation. Then, Taylor series positioning based trilateration is applied to fuse the 1D target states (filtered by 1D tracker) from all the receivers to provide 2D Cartesian coordinates of targets – assuming that there is no ambiguity in this association, an important assumption that will be discussed below in Section 4.1. Finally, 2D tracking employs 2D global nearest neighbor tracker to provide the Cartesian tracks. In the following subsections, the details of the algorithm will be presented.

2.1.1 Target detection

The pre-processing mainly consists of background subtraction, bandpass filtering and upsampling. There are different approaches to remove static clutter, such as basic averaging [24] and exponential averaging [15]. Con-sidering the relatively static property of the indoor clutter, double canceler (3-order moving target indication filter) [46] is suited to this application due to its simplicity and efficiency. Then, the bandpass FIR filter removes noise beyond operational frequency band. As last, upsampling in fast time is applied to improve the accuracy of time-of-arrival estimation. The pre-processing and detection procedure are illustrated in Fig. 2.2. An exam-ple of the signal after pre-processing is illustrated in Fig. 2.3, where the

Detection result Background

subtraction

CA-CRAR

Raw data Bandpass

filter Upsampling

TOA estimation

Figure 2.2: Pre-processing and detection

0 5 10 15 20 25 30 -0.02 -0.01 0 0.01 0 5 10 15 20 25 30 -0.02 0 0.02 A m p lit u d e 0 5 10 15 20 25 30 -0.02 -0.01 0 0.01 Fast time[ns] Static clutter Human reflection

Figure 2.3: Measured signals of a moving human target at different time (Blue signal: before MTI; red signal: after MTI)

backscattering of the moving target at different slow time is illustrated. It also indicates that the human target, whose response is distributed over a certain period (appr. 5 ns), is a typical extended target owing to the high range resolution of UWB signal.

The target detection can be performed by a standard cell-average con-stant false alarm rate (CA-CFAR) procedure [46]. CFAR (scheme see Fig.2.4) is a classical approach to detect the target presence in noise and

Mean

X

Figure 2.4: Constant false alarm rate detection scheme

clutter. CFAR, based on Neyman-Pearson criterion, provides adaptive es-timation of detection threshold, supposing that the probability density of noise is known. Although, in our case, the actual problem is the detec-tion of an extended target, for which more elaborate procedures do exist (Order-Statistics CFAR [47], for instance), we found that CA-CFAR is ro-bust and efficient at the current stage of our research. In order to improve the extended target detection performance, the length of the reference cells and guard cells should be designed carefully. Considering a person with a normal size, the length of the guard cells should be longer than 1 m, and reference cells should have a length of about 1 to 2 m.

Although the high range resolution of UWB signal is useful for target signature analysis, only target TOA is required for localization/tracking purposes. Generally, TOA estimation for human target in UWB radar has not been deeply discussed. Leading edge detection based TOA estimation has been investigated in [48]. Due to the specific motion characteristics of humans, leading edge detection may, in fact, indicate the arms and feet that normally moves ahead of torso, so using the geometrical center as the TOA is considered in this thesis instead of using the leading edge. An averaging filter is introduced, after CA-CFAR, to estimate the scattering center of the extended target from multiple detection plots. After CFAR,

one range profile is replaced by a vector with binary values (0 or 1) . Then the average filter goes through the binary samples of the vector one by one, and replaces the amplitude of each sample with the average of the adjacent binary values. In this way, the energy of multiple samples resulting from one extended target is accumulated. The equation of one-dimension average filter is given by:

y (n) = 1 2N + 1 n+N X i=n−N x (i), (2.1)

where y (n) is the average filter result at time n, x (i) is the binary value at time i, and the length of the filter is 2N + 1. The peak of the filtered result can be seen as the sample that has the largest probability to be the scattering center of this target. Thus the peak of the filter output is determined as the target time-of-arrival.

After target detection, the time-of-arrival of the extended target in each receiver is sent to the 1D tracker for bistatic tracking.

2.1.2 1D bistatic tracking

Radar tracking algorithm generally consists of data association, track main-tenance and tracking filtering. While data association serves as a tool for connecting the plots with corresponding targets, track maintenance is re-sponsible for the management of tentative tracks and confirmed tracks. Tracking filtering uses the associated plots obtained from data association to update target tracks.

The 1D tracking scheme is illustrated in Fig.2.5. First, the global near-est neighbor association algorithm [35] conducts the measurement-to-track association to confirmed and tentative tracks. Second, the associated plots are used by Kalman filter to update the tentative and confirmed tracks, respectively. Then, if there are still any unassociated plots, they are used to form new tentative tracks. Finally, the tracks satisfying the deletion criterion are removed, and the tentative tracks satisfying the confirmation criterion are promoted to be confirmed tracks. The most well-known cri-terion for tentative track confirmation is called M/N rule, which indicates that during the last N updates, at least M plots must have been associated with this tentative track - with M = 3 and N = 5 being typical values. M/N rule is also a common criterion for track deletion. If this track has not been updated for at least M times during the last N updates, this

Plot-to-comfirmed tracks association Detection plots Plot-to-tentative tracks association Tracks filtering Not associated plots Not Associated plots Associated plots New tentative tracks initialization Unupdated tracks Deletion Tentative tracks comfirmation Associated plots

Figure 2.5: 1D bistatic tracking scheme

target is probably lost, and this track should be deleted. The design of the track maintenance logic is important due to its impact on clutter/multipath rejection.

Once the plots are associated with tracks, the tracking filter updates the target tracks with these new plots. The constant velocity Kalman filter is applied for 1D tracking, since the velocity of human can be considered approximately to be constant during a short pulse repetition interval, typ-ically tenths of seconds. Note that the used Kalman tracker does not take into account the Doppler information for simplicity. An example of the simulated 1D tracking result can be found in Fig.2.6.

Kalman filter is a well-known technique, so general descriptions for state estimation, covariance, etc. are not mentioned here. The constant-velocity motion model [46] adopted in the Kalman filter is described by:

• State model:  rk ˙rk  =  1 T 0 1   rk−1 ˙rk−1  +  T2/2 T  wk−1, (2.2)

0 1 2 3 4 5 6 7 4 5 6 7 8 9 10 Slow time [s] B is ta tic ra ng e [m] Clutter Tentative track Confirmed track

Figure 2.6: Simulated 1D bistatic tracking

respectively. T is assumed to be a constant time interval between two adjacent states, and w is white noise with variance δ2

w.

Then the state error covariance Q is given by:

Q =  T4_{/4 T}3_/2 T3/2 T2  δ_w2. (2.3) • Measurement Model: Zk=  1 0   rk ˙rk T + vk, (2.4)

where Zkis the bistatic range measurement at time k, and vkis white

noise with variance δ_v2. • Initial Condition: r0 =  Z2 Z2−Z₂ 1 T . (2.5) 2.1.3 Trilateration

After 1D tracking, most of the clutter and multipath can be rejected, and the trilateration algorithm can be applied to locate targets in the 2D Carte-sian coordinates. In this section, we assume that all the 1D tracks in dif-ferent receivers have already been associated correctly, and the association issue will be discussed later in Section4.1. It is worth pointing out that if the tracks, which are originated from different receivers but belong to the same target, are not associated properly, unwanted ghost targets might be created by trilateration.

The trilateration in multistatic radar estimates the 2D coordinates of the target based on 1D range measurements from all the receivers. Let’s consider a multistatic UWB radar with one transmitter and N receivers. The positions of the transmitter and receivers are known as (xt, yt) and

(xi, yi), i = 1, 2, . . . , N . Then the bistatic range of the target corresponding

to receiver i is given by:

di =

q

(x − xi)2+ (y − yi)2+

q

(x − xt)2+ (y − yt)2, (2.6)

where (x, y) are the Cartesian coordinates of the target. The positioning problem then is equal to finding the solution of the non-linear equation set Eq. (2.6). Two different methods, the direct calculation and Taylor series positioning, are discussed in the following.

Direct calculation

Considering an ideal scenario in which no noise exists, the direct calculation method can be used to find the solution of Eq. (2.6). Shifting the second term of Eq. (2.6) to the left side and squaring, we obtain (derivation see AppendixC.1):

(xi− xt) x + (yi− yt) y + pi = di

q

(x − xt)2+ (y − yt)2, (2.7)

where pi = 1₂ d2i + xt2− x2i + yt2− yi2
. Supposing that there are two

re-ceivers (N = 2), we have:    (x1− xt) x + (y1− yt) y + p1 = d1 q (x − xt)2+ (y − yt)2 (x2− xt) x + (y2− yt) y + p2 = d2 q (x − xt)2+ (y − yt)2 . (2.8)

Multiplying the first equation of (2.8) with d2, the second equation of (2.8)

with d1, then subtract them we get:

Ax + By = C , (2.9) Where    A = d2(x1− xt) − d1(x2− xt) B = d2(y1− yt) − d1(y2− yt) C = p2d1− p1d2 . Then we have: y = C − Ax B . (2.10)

Substituting Eq. (2.10) into the first equation of (2.8) yields: (x1− xt) x + (y1− yt) C − Ax B + p1 = d1 s (x − xt)2+  C − Ax B − yt 2 (2.11) Simplifying Eq. (2.11), we have (derivation see Appendix C.3):

Dx2+ Ex + F = 0, (2.12) where      D =(x1− xt) −A_B(y1− yt) 2 − (1 +_BA22)d21 E = 2(x1− xt) −A_B(y1− yt)  _C B(y1− yt) + p1 + 2xt+ 2AC B2 −2A_Byt
d21 F =_C B(y1− yt) + p1 2 − d2 1  x2 t+ y2t −2CByt+ C2 B2  .

The two solutions of Eq. (2.12) are:

ˆ

x = −E ± √

E2_{− 4DF}

2D . (2.13)

Finally, the estimation of the target position (ˆx, ˆy) can be given as: ( ˆ x = −E± √ E2_−4DF 2D ˆ y = C−Aˆ_B x . (2.14)

The positioning result of the direct calculation method for a general antenna topology is derived as above. A more specific topology commonly used in multistatic through-wall radar is shown in Fig.1.3, where the trans-mitter and two receivers are deployed on a line parallel to the wall. For such a specific set-up, the result of the direct calculation method can be simpli-fied. The false intersection that represents the wrong target location can be removed, since it always appears behind the radar system. Thus only one deterministic solution of Eq. (2.7) is obtained. The direct positioning solution in such a specific set-up can be found in Appendix C.2.

Taylor series positioning

Taylor series method [31] has been introduced to solve the non-linear equa-tion set Eq. (2.6), due to its good estimation accuracy. It works as follows.

The update of the target position is given by: 

x0 = x + δx

y0= y + δy , (2.15)

where (x, y) is the initial position of the target and (δx, δy) denotes the

distance estimation error in X and Y axis. Then, once the predefined initial position (x0, y0) is given, the iteration process updates the target

position by Eq. (2.15) with the error (δx, δy) computed by Eq. (2.16), until

the distance estimation errors are sufficiently small in comparison with a predefined threshold. After some iterations the solution of Eq. (2.15) provides the trilateration result.

For each iteration, distance error (δx, δy) is given by:



δx δy  = ATA

−1

ATB, (2.16)

where A and B are temporary variables and defined as:

A =  a11 . . . aN 1 a12 . . . aN 2 T , (2.17) and B = d1− rT − rR1 . . . dN − rT − rRN T . (2.18)

The sub-elements of matrix A and B are given by:

ai1= x − xt rT +x − xi rRi , ai2= y − yt rT +y − yi rRi , and    rT = q (x − xt)2+ (y − yt)2 rRi= q (x − xi)2+ (y − yi)2 .

In general, the Taylor series method demonstrates higher positioning accuracy than classical direct calculation method [31], but it is more com-putationally expensive and the initial position should be chosen carefully – otherwise divergence may happen. In general, Taylor series method suits well to the short range applications, since choosing the initial position is less critical in such cases.

2.1.4 2D Cartesian tracking

After trilateration, 2D tracking is conducted to associate the plots, and present 2D target trajectories. It has a similar structure with 1D tracking. Thus in this section only the 2D tracking filter is described.

The nearly constant velocity Kalman filter model [46] is applied, and it can be described by:

• State model: Xk = F Xk−1+ Gwk−1, (2.19) where Xk =  xk x˙k yk y˙k T

is the target state at time k. w ∼ N 0, δ2_w
represents the target acceleration.

G =  T2/2 T T2/2 T T relates the system error to the target motion. F denotes the state transition matrix:

F =     1 T 0 0 0 1 0 0 0 0 1 T 0 0 0 1     .

Then the state error covariance is described as

Q = E h Gw (Gw)T i = δ_w2 · QD · 12×2, where QD =  T4/4 T3/2 T3_{/2 T}2  . • Measurement Model: Zk= HXk+ vk, (2.20)

where Zk is the position measurement at time k. v ∼ N (0, R) is the

measurement error with covariance R = δ_v2 · diag (1, 1). H denotes the measurement matrix:

H =  1 0 0 0 0 0 1 0  .

• Initial Condition: Target initial state:

X0=



Zx2 (Zx2 − Zx1) /T Zy2 (Zy2 − Zy1) /T T

. (2.21) Initial prediction error covariance matrix can be modeled as

P0= δ2v· diag (PD, PD) , where PD =  1 1/T 1/T 2/T2  .

2.2

Decentralized tracking result

2.2.1 Simulation result

In the simulation, a single-target scenario is considered to compare the performance between the centralized and the decentralized approach. The centralized algorithm applied here, whose structure is similar to the central-ized algorithm proposed in [22], consists of target detection, Taylor series positioning and 2D tracking. The parameters in the simulation are derived from technical specs of the multistatic UWB radar (see AppendixA). Two receivers (Rx1 and Rx2) and one transmitter (Tx) are located linearly with a separation of 1 m. The pulse repetition interval is equal to 0.12s. The target bistatic ranges in Rx1 and Rx2 are the input of the simulation and disturbed by Gaussian white noise. The parameters of the 2D Kalman fil-ter in the decentralized approach are the same as those in the centralized approach, which makes the comparison fair. Also note that in the following simulations, the basic detected plots for the two approaches are the same, and that only the processing is different.

Two scenarios are considered in the simulation. In the first scenario, a point target is moving at a constant velocity (0.4 m/s) along a straight line (see Fig.2.7ato2.7d). In the second scenario, a point target is moving at the same constant velocity (0.4 m/s) along a square route (in Fig.2.7e

and2.7f). Both simulated results indicate that the decentralized algorithm outperforms the centralized algorithm in terms of smaller tracking error. Additionally, the decentralized algorithm benefits the 2D measurement-to-track association, since trilateration positions of different targets are less likely to overlap. If multiple targets exist in Fig. 2.7c and 2.7e, the 2D

measurement-to-track association in the centralized algorithm would be more difficult than that in Fig.2.7dand 2.7f, since the positioning error is much larger.

100-time Monte Carlo simulation was conducted to compare the root mean square error (RMSE) of the two approaches for the square track. Fig.2.8ashows that 1D tracker significantly improves the positioning accu-racy. In Fig.2.8b, the overall error of the decentralized algorithm is slightly smaller than that of the centralized algorithm. This probably results from the fact that the tracking error is partly compensated by the track recovery capability of the 2D Kalman filter in the centralized algorithm.

Concerning the computation time, the Monte Carlo simulation time of the decentralized approach (0.62s) is longer than that of the centralized approach (0.34s). This is due to the fact that the additional 1D tracker makes the decentralized method more computationally costly. However, as can be expected, the increase of the computational complexity can be partially compensated by the decrease of complexity in positioning due to efficient false alarm rejection of 1D tracking.

2.2.2 Experimental verification

The decentralized approach was experimentally verified in an indoor envi-ronment. The multistatic UWB radar described in Appendix A was used (see Fig. 2.9a). A person was walking in a room along a predefined path. One range profile acquired with receiver one is shown in Fig. 2.9b, where multipath can easily be seen.

The CA-CFAR and average filter results are shown in Fig.2.10, where the target, clutter and multipath can be easily distinguished. Two false alarm probabilities (10−5 and 10−7) have been tested. The higher the false alarm probability is, the more clutter/multipath can be found. The length of the reference cells and guard cells are 2 m and 1 m, assuming that the size of a person is about 1 m. And the length of the average filter is set to 1 m due to the same reason.

The centralized/decentralized algorithms are compared in Fig. 2.11. The parameters used in 1D/2D Kalman filter are set to (δw = 0.6, δv = 0.3)

and (δw = 0.05, δv = 0.5), respectively. The major track error is mainly due

to poor accuracy of detection plots. Since human is an extended target, the detection may capture different body parts as the final CFAR output. It is worth pointing out that the error in cross-range is also affected by multistatic antenna topology. Larger antenna separation distance, or using

-5 -4 -3 -2 -1 0 1 2 3 4 5 0 1 2 3 4 5 6 7 8 X [m] Y [ m ] Target Trajectory Rx2 Tx Rx1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 PDetection results12 True trajectory Tracking trajectory

(a) Centralized approach (δ2= 0.3m)

-5 -4 -3 -2 -1 0 1 2 3 4 5 0 1 2 3 4 5 6 7 8 X [m] Y [ m ] Target Trajectory Rx2 Tx Rx1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 PDetection results12 True trajectory Tracking trajectory (b) Decentralized approach (δ2 = 0.3m) -4 -3 -2 -1 0 1 2 3 4 5 0 1 2 3 4 5 6 7 8 X [m] Y [ m ] Target Trajectory Rx2 Tx Rx1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P1Detection results2 True trajectory Tracking trajectory (c) Centralized approach (δ2_{= 0.7m)} -5 -4 -3 -2 -1 0 1 2 3 4 5 0 1 2 3 4 5 6 7 8 X [m] Y [ m ] Target Trajectory Rx2 Tx Rx1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 PDetection results12 True trajectory Tracking trajectory (d) Decentralized approach (δ2 _{= 0.7m)} -2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 Target Trajectory X [m] Y [m] Unassociated measurement

(e) Centralized approach (δ2= 0.3m)

-2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 Target Trajectory X [m] Y [m] (f ) Decentralized approach (δ2= 0.3m)

Figure 2.7: 2D tracking result with different noise variance δ2 _{(red line} -ground truth, green dots - positioning results, blue line - target track)

0 5 10 15 20 0 0.2 0.4 Observation time [s] R M SE [ m ] 0 5 10 15 20 0 0.05 0.1 0.15 Observation time [s] R M SE [ m

] (a) RMSE of Taylor series positioning

0 5 10 15 20 0 0.2 0.4 Observation time [s] R M SE [ m ] 0 5 10 15 20 0 0.05 0.1 0.15 Observation time [s] R M SE [ m ] (b) RMSE of 2D tracking

Figure 2.8: Error comparison between centralized /decentralized algorithm (red line - centralized approach, blue line - decentralized approach).

(a) Measurement set-up

0 10 20 30 40 50 60 70 -3 -2 -1 0 1 2 3x 10 -3 6ORZtime [ns] M ag ni tud e

Reflection from human

Multipath

(b) Range profile of human backscattering

Figure 2.9: Hardware setup and the pre-processed signal

more receiving antennas, would provide lower cross-range error. It can be seen from Fig. 2.11 that the tracking performance of the decentralized al-gorithm is better than that of the centralized alal-gorithm in terms of smaller tracking error.

Fig. 2.11a and 2.11d demonstrate that the 1D tracker performs as an effective clutter filter, and the multipath and clutter have almost been

re-Slow time [s] B is ta tic ra ng e [m] 0 5 10 15 20 2 3 4 5 6 7 8 9 10 11

(a) CFAR result (Pf a= 10−5)

Slow time [s] B is ta tic ra ng e [m] 0 5 10 15 20 2 3 4 5 6 7 8 9 10 11

(b) Average filter result (Pf a= 10−5)

Slow time [s] B is ta tic ra ng e [m] 0 5 10 15 20 2 3 4 5 6 7 8 9 10 11 (c) CFAR result (Pf a= 10−7) Slow time [s] B is ta tic ra ng e [m] 0 5 10 15 20 2 3 4 5 6 7 8 9 10 11

(d) Average filter result (Pf a= 10−7)

Figure 2.10: Target detection result in receiver one with different false alarm probability

jected. This is due to the use of the historical target motion information(i.e., the 1D tracks). The tentative tracks formed by clutter and multipath are not able to be promoted as confirmed tracks, since they are not stable enough within a certain duration. The 1D Kalman filter also increases the accuracy of the bistatic range measurement.

In order to show the clutter rejection capability of the decentralized ap-proach, the CA-CFAR detection results with higher false alarm probability (Pf a = 10−5) are processed by two tracking algorithms. The results shown

in Fig.2.11dto2.11findicate that the decentralized method is more robust than the centralized method in dense clutter environments. It also shows good potential to improve 2D measurement-to-track association due to its smaller error.

results from the fact that 1D tracking takes advantage of the historical bistatic range information of adjacent range profiles. In this way, clut-ter and multipath can be rejected significantly, which leads to a reduced number of ghost targets and partially solves the measurement-to-track as-sociation problem in 2D Cartesian tracking.

2.3

Conclusion

A global decentralized framework for human target tracking in multistatic UWB radar has been developed. The algorithm involves moving target indication filtering of clutter echoes, decentralized detection and tracking based on range profiles. In comparison with the conventional centralized algorithm, both the simulated and experimental results show the potential that decentralized approach is more robust and has smaller tracking error.

0 5 10 15 20 2 3 4 5 6 7 8 9 10 11 Slow time [s] B is ta tic ra ng e [m] (a) 1D tracking in Rx1 (Pf a= 10−7) -2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 X [m] Y [m] (b) Centralized approach (Pf a= 10−7) -2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 X [m] Y [m] (c) Decentralized approach (Pf a= 10−7) 0 5 10 15 20 2 3 4 5 6 7 8 9 10 11 Slow time [s] B is ta tic ra ng e [m] (d) 1D tracking in Rx1 (Pf a= 10−5) -2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 X [m] Y [m]

(e) Centralized approach (Pf a= 10−5)

-2 0 2 0 2 4 6 Rx2 Tx Rx1 P1 P2 P3 P4 X [m] Y [m] (f ) Decentralized approach (Pf a= 10−5)

Figure 2.11: Centralized/decentralized tracking results (green dot - associ-ated plot, red dot - plot of tentative track, blue line - confirmed track)

Range-Doppler processing

for human target in

multistatic UWB radar

3.1

Introduction

3.1.1 Motivation

UWB radar is promising for indoor human detection and tracking, due to its fine down-range resolution [1]. While the range information-based hu-man tracking in UWB radar has already been addressed in the literature [14,15,52], the possibility of detecting/tracking personnel in range-Doppler domain by UWB radar has not been examined yet. In a range-Doppler im-age, multiple scatterers/targets are not only resolved in range, but also in Doppler. In principle, a fine down-range resolution (due to UWB signal) and a fine Doppler resolution (generated by proper range-Doppler process-ing) are both desirable to improve the performance of human detection, tracking and classification. This range-Doppler information will also prove to be useful for target association in multistatic radar networks.

Although UWB radar has often a high range resolution, its Doppler resolution is rather limited if conventional Fourier Transform (FT) based range-Doppler processing is applied, since a target may traverse multi-ple range cells in one coherent processing interval (CPI). Therefore, in this chapter, we propose two range migration compensation approaches

for range-Doppler processing in UWB radar: the hypothesis-testing (HT) based algorithm and the Keystone Transform (KT) based algorithm. Some typical radar systems are classified according to their range/Doppler reso-lution in Fig.3.1, where UWB radar presents a high range resolution and quite limited Doppler resolution. The goal of the range migration compen-sation is to improve the Doppler resolution of UWB radar (move it along the vertical axis in Fig. 3.1) by eliminating the Doppler widening effect, and also decrease the target blurring in the range-Doppler image.

100 10 1 0.1 Range resolution [m] 1 0 0 1 0 1 0 .1 D o p p le r re so lu ti o n [ H z] Maritime surveillance HRRP UWB Ground surveillance SAR/ISAR Short-range CW UWB Range migration compensation Air surveillance

Figure 3.1: The classification of radar systems with respect to

range/Doppler resolution

This chapter is organized as follows. The human target backscattering model is introduced in Section 3.1.2. The conventional FT-based range-Doppler processing in narrow-band radar is described in Section3.1.3. Hy-pothesis testing and Keystone Transform based range migration compensa-tion approaches are proposed in Seccompensa-tion3.2and Section3.3, respectively. In Section3.4, a new concept, range-Doppler video sequence, is proposed and compared with high-resolution range profiles and micro-Doppler images. Finally, conclusions are drawn in Section3.5.

3.1.2 Human radar backscattering model

Two different human motion models have commonly been used in the liter-ature: mathematical parametric model [53] and empirical non- parametric model [54]. The parametric model (see Fig. 3.2) is designed for some spe-cific motions(i.e., walking and running), and users are able to manually adjust the motion parameters, such as speed and human height. One of the drawbacks for the parametric model is that only limited types of the motion model are publicly available.

(stroller or saunterer). The walker moves his arms during motion, whereas the stroller’s arms hang by his sides.

The radar equation is used to calculate the reflection of each human body part and adds the reflections at each time sample. The radar data processing model calculates the simulated spectrogram from it.

The fit-function calculates the difference between the measured spectrogram and the simulated spectrogram. The fit-function to be used depends upon the noise properties and reflected radar signal properties. The difference is a function of the parameters of the walking model. By minimising the difference as a function of the parameters of the walking model, the best fit is found. The parameters that belong to the best fit describe the human walking behaviour. The scene of the walking person in a virtual environment is generated given the estimated walking parameters. The simulated walking person generates an image of the person at the measured range. Successive images of the animated person are shown inFig. 2.

3 The human model

Human walking models have been particularly developed for realistic animation in virtual reality[5, 6]. Compu-tation of a realistic simulated spectrogram on the basis of a simulated walking human is our goal. This requires detailed information about the time-varying human body parts.

3.1 Shape and size of human body parts

The human model is modelled as having 12 body parts (see

Fig. 3). The head is a sphere. The other human body parts

are cylinders or ellipsoids. The size of the human body parts are equivalent to average human body sizes (seeTable 1).

The human body parts are connected to each other by means of time-dependent translations and rotations (see

Table 2). These translations and rotations depend on the size

of the human. The positions of the human body parts are calculated using the Thalmann model with quaternions. The position of each human element is a multiplication of

Fig. 1 Model-based approach

Fig. 3 Human model with 12 body parts, 3 translation

trajectories and 14 rotation trajectories

Figure 3.2: Thalmann model [53]

The empirical non-parametric models, which are based on human mo-tion capture data, are usually more realistic than the parametric model. More motion types(e.g., jumping, dancing, fighting) are available for re-search. In the motion capture database of Carnegie Mellon University [41], a motion capture system consisting of 12 infrared cameras and 41 mark-ers attached to a human body (see Fig. 3.3), is used to record motions. Datasets of various motion types are provided.

For this study, the empirical non-parametric model is selected. We choose five most crucial markers (i.e., torso, left hand, right hand, left foot, right foot), and an equal RCS is assumed for the reflection from all the markers for simplicity. Then we construct range profiles by coherently summing the echoes from different parts of the human body. According to the human kinematic studies [55], one complete human gait can be de-scribed by four main phases (see Fig. 3.4): (1) double support (both feet