universiti putra malaysiapsasir.upm.edu.my/id/eprint/71436/1/fk 2018 99 ir.pdfterkini. tesis ini...

UNIVERSITI PUTRA MALAYSIA

PASSIVE FORWARD SCATTER RADAR BASED ON LTE SIGNAL FORVEHICLE DETECTION AND CLASSIFICATION

NOOR HAFIZAH BINTI ABDUL AZIZ

FK 2018 99

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PASSIVE FORWARD SCATTER RADAR BASED ON LTE SIGNAL FOR VEHICLE DETECTION AND CLASSIFICATION

By


Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Doctor of Philosophy

June 2018

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COPYRIGHT

All material contained within the thesis, including without limitation text, logos,

icons, photographs, and all other artwork, is copyright material of Universiti Putra

Malaysia unless otherwise stated. Use may be made of any material contained within

the thesis for non-commercial purposes from the copyright holder. Commercial use

of material may only be made with the express, prior, written permission of

Universiti Putra Malaysia.

Copyright © Universiti Putra Malaysia

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DEDICATION

This thesis is dedicated to:

My late father,

Allahyarham Hj. Abdul Aziz B Abdul Ghani

Alfatihah

My beloved mother,

Hjh. Kamariyah Binti Mohd Salleh

My brothers and sisters,

and to all of my friends.

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment

of the requirement for the degree of Doctor of Philosophy

PASSIVE FORWARD SCATTER RADAR BASED ON LTE SIGNAL FOR VEHICLE DETECTION AND CLASSIFICATION

By


June 2018 Chairman : Professor Raja Syamsul Azmir b. Raja Abdullah, PhD Faculty : Engineering Passive bistatic radar (PBR) system can utilise suitable signal from illuminator of

opportunity to improve radar proficiency. In conventional PBR, a heterodyne

receiver and concept is employed. This is due to the PBR requiring reference signal

from a direct transmission for synchronization, and then the signal is down converted

to the base band where target detection is evaluated by analyzing the ambiguity

function of the received signal. On the other hand, by using the forward scattering

phenomena and technique in specific mode of PBR, the operation can be further

enhanced and improved especially in target detection and classification. This specific

system is identified as the passive forward scattering radar (FSR). One of the main

condition of passive FSR is the operational mode limited to within the radar

baseline. In this scenario, the desired received signal is formed through the

shadowing of a direct signal by the target shape rather than back scattering signal

from the target such as in conventional radar system. Passive forward scattering

radar system offers a number of advantages including enhanced target cross section,

long coherent intervals of the receiving signal, absence of signal fluctuations,

reasonably simple hardware, economical as it does not need a transmitter system and

a spectrum allocation, practically unseen to surveillance receivers, portable due to its

smaller size, no synchronization needed for reference signal from direct signal of

illuminator, straightforward signal pre-processing for target detection and enhanced

classification capability.

Thus, the aim of this thesis is to develop and prove the concept of passive forward

scattering radar especially for ground detection and classification. The objective is to

implement the experimental analysis and results for vehicle detection and

classification by exploiting the latest 4G LTE technology signal. This thesis clarifies

in details the LTE based passive forward scatter radar receiver circuit, the detection

scheme and the classification algorithm. In addition, the proposed passive forward

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scatter radar circuit employed the self-mixing technique at the receiver, hence it did

not require a synchronization signal from the LTE base station. The classification

capability in passive forward scattering radar increases the effectiveness of the

system.

Three vehicle categories with different sizes and shapes were tested, namely

Compact, Saloon and Small Sport Utility Vehicle (SUV). The passive FSR

experiment results show that the vehicles were successfully detected, even by raw

received signals without any complicated signal processing techniques and the

proposed classification system provided outcomes of satisfactory classification

performance. The Doppler spectrum scattered by the vehicle is used as the features

for input to the classification system. The classification algorithm was developed

based on Principal Component Analysis (PCA) and k-Nearest Neighbours (k-NN).

In addition, the baseline crossing range distance from the radar receiver, as well as

vehicle’s speed, was successfully predicted.

In general this thesis presents the first classification analysis and performance for the

passive forward scatter radar system. The great potential of the passive forward

scatter radar system provides a new research area in passive radar that can be used

for diverse applications. The thesis also proved that if the FSR mode can be

integrated or hybrid with the conventional passive radar system and still have similar

performance competency, the system can achieve tremendous improvement. Hence,

it opens up a new frontier in passive radar that can be used for many applications

including border protection, microwave fences, building monitoring, traffic

surveillance, and so forth.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

RADAR PENYERAKAN KE HADAPAN PASIF BERDASARKAN SIGNAL LTE BAGI PENGESANAN DAN KLASIFIKASI KENDERAAN

Oleh


Jun 2018

Pengerusi : Profesor Raja Syamsul Azmir b. Raja Abdullah, PhD Fakulti : Kejuruteraan Sistem radar bistatik pasif (PBR) boleh menggunakan isyarat yang sesuai dari

pemancar yang berpeluang untuk meningkatkan penguasaan radar. Di dalam PBR

konvensional, penerima dan konsep heterodin digunakan. Ini adalah disebabkan oleh

PBR memerlukan isyarat rujukan daripada penghantaran langsung untuk

penyegerakan, dan isyarat itu kemudian ditukar kepada jalur asas di mana

pengesanan sasaran dinilai dengan menganalisis fungsi kekaburan daripada isyarat

penerimaan. Sebaliknya, dengan menggunakan fenomena dan teknik penyerakan ke

hadapan dalam mod tertentu PBR, operasi dapat dipertingkatkan dan diperbaiki

terutama di dalam pengesanan dan klasifikasi sasaran. Sistem ini dikenal pasti

sebagai radar penyerakan ke hadapan (FSR) pasif. Salah satu syarat utama bagi FSR

pasif adalah mod operasi terhad kepada tempoh asas radar. Di dalam senario ini,

isyarat penerimaan yang dikehendaki terbentuk melalui bayangan isyarat langsung

oleh bentuk sasaran dan bukannya daripada isyarat penyerakan yang terbias daripada

sasaran seperti dalam sistem radar konvensional. Sistem radar penyerakan ke

hadapan pasif menawarkan beberapa kepentingan, termasuk bahagian silang sasaran

dipertingkatkan, keteguhan kepada sasaran tersembunyi, isyarat penerimaan

mempunyai sela koheren yang panjang, tiada turun naik isyarat, perkakasan yang

mudah dan munasabah, menjimatkan kerana ia tidak memerlukan sistem pemancar

dan peruntukan spektrum, boleh dikatakan ghaib kepada penerima pengawasan,

mudah alih kerana saiz yang lebih kecil, tidak ada penyelarasan yang diperlukan bagi

isyarat rujukan dari isyarat langsung sistem pemancar, isyarat mudah diproses untuk

mengesan sasaran dan kemampuan pengklasifikasian dapat dipertingkatkan.

Oleh itu, tujuan laporan ini dilaksanakan adalah untuk membangunkan dan

membuktikan konsep radar penyerakan ke hadapan pasif terutamanya bagi

pengesanan dan pengelasan sasaran di atas darat. Objektif adalah untuk

melaksanakan penganalisaan secara eksperimen dan keputusannya untuk pengesanan

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dan klasifikasi kenderaan dengan mengeksploitasi isyarat teknologi 4G LTE yang

terkini. Tesis ini menjelaskan secara terperinci mengenai litar radar penyerakan ke

hadapan pasif berasaskan penerima LTE, skim pengesanan dan algoritma

pengelasan. Di samping itu, litar radar penyerakan ke hadapan pasif yang

dicadangkan menggunakan teknik pencampuran diri di dalam alat penerima, oleh itu

ia tidak memerlukan isyarat penyegerakan dari stesen pangkalan LTE. Keupayaan

pengelasan di dalam radar penyerakan ke hadapan pasif dapat meningkatkan

keberkesanan sistem.

Tiga kenderaan yang bersaiz dan berkelajuan berbeza telah diuji, iaitu Compact,

Saloon dan Small Utility Vehicle (SUV). Keputusan eksperimen daripada FSR pasif

menunjukkan bahawa kenderaan telah berjaya dikesan, walaupun dengan isyarat

kasar yang diterima tanpa sebarang teknik pemprosesan isyarat yang rumit dan

sistem klasifikasi yang dicadangkan memberikan hasil prestasi klasifikasi yang

memuaskan. Penyerakan spektrum Doppler daripada kenderaan digunakan sebagai

ciri-ciri untuk input kepada sistem klasifikasi. Algoritma pengelasan telah

dibangunkan berdasarkan ketumpatan kuasa spektrum (PSD), Analisis Komponen

Prinsipal (PCA) dan k-kejiranan yang terdekat (k-NN). Di samping itu, jarak garis

dasar pelbagai lintasan daripada penerima radar, serta kelajuan kenderaan telah

berjaya diramalkan.

Secara umum tesis ini membentangkan analisis klasifikasi dan prestasi yang pertama

untuk sistem radar penyerakan ke hadapan pasif. Sistem radar penyerakan ke

hadapan pasif berpotensi besar di dalam menyediakan ruang penyelidikan baru di

dalam radar pasif yang boleh digunakan untuk pelbagai aplikasi. Tesis ini juga

membuktikan bahawa jika mod FSR yang boleh disepadukan atau hibrid dengan

sistem radar pasif konvensional dan masih mempunyai kecekapan prestasi yang

sama, sistem boleh mencapai peningkatan yang amat besar. Oleh itu, ia membuka

satu era baru dalam radar pasif yang boleh digunakan untuk pelbagai aplikasi

termasuk perlindungan sempadan, pagar mikro, pemantauan bangunan, pengawasan

lalu lintas, dan sebagainya.

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ACKNOWLEDGEMENTS

In the name of Allah, the Most Gracious and the Most Merciful.

Firstly, I would like to thanks to Allah the Al-Mighty for giving me the chance to get

through this journey and give me the strength, patience, courage and determination

for completing this work, Alhamdulillah. This work accomplished with the support

and encouragement of numerous people including my well-wishers, my friends and

colleagues who deserved the appreciation.

I would like to express my special appreciation and thanks to my supervisor

Professor Dr. Raja Syamsul Azmir b. Raja Abdullah for inspiration, advice and

guidance from the beginning of this research even providing the experiences and

understandings throughout the research work. His invaluable help of constructive

comments and suggestions throughout the experimental and thesis works have

contributed to the success of this research. My appreciation also goes to Dr.

Fazirulhisyam b. Hashim and Dr. Nur Emileen Abdul Rashid for their brilliant

comments and suggestions.

My deepest gratitude and appreciation to my beloved parents and family for their

endless support, encouragement, prayers, constant doa’ and love. Words cannot

express how grateful I am to my late Father, Allahyarham Haji Abdul Aziz Abdul

Ghani (Al-Fatihah), was a great inspiration to me, and his love for knowledge was

the founding grounds for my love of knowledge and always encouraged me to reach

for the stars. My Mother, Hajjah Kamariyah Mohd Salleh has been such a source of

strength, support and encouragement throughout this journey and thank you for all of

the sacrifices that you’ve made on my behalf. Your prayer for me was what sustained me thus far. A special thanks to my brothers Mohd Shawalludin and Mohd

Fadzil, my sisters in law for their endless encouragements and supports.

I also would like to thank and acknowledge The Ministry of Education, Malaysia,

and the University of Technology MARA (UiTM) who provided me with a

scholarship, grant and facilities to undertake this study. I would like to convey my

appreciation to Malaysia Agro Exposition Park Serdang (MAEPS) for giving me a

permission to conduct the experimental work at their parking area.

My special words of gratitude to Prof. Asem & Dr. Nour for all their supports and

motivations during my wonderful days of PhD study. I would also like to thank all

my friends who supported me in writing, experimental work and incented me to

strive towards my goal, especially Hanisah Sensei, Azizi, Aicha Midori, and Kama. I

also appreciate the truly wonderful friends that I have been blessed with, specifically

Ustazah Ramziah, Eliza, Suri Mawarne Kaidar, Suhaila, Nor, Zue, Oja, Hanim,

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Remy, Sherliza, Nana, Faridah, Fadzilah, Ayunie, Ogy, Ruby, Ed, ARC members

and many others.

At the end I would like express appreciation to all those who have lend a hand and

supported me directly and indirectly, your kindness means a lot to me. Thank you

very much. Arigatou gozaimasu.

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfillment of the requirement for the degree of Doctor of Philosophy.

The members of the Supervisory Committee were as follows:

Raja Syamsul Azmir b. Raja Abdullah, PhD Professor

Faculty of Engineering Universiti Putra Malaysia

(Chairman)

Fazirulhisyam b. Hashim, PhD Senior Lecturer

Faculty of Engineering Universiti Putra Malaysia

(Member)

Nur Emileen Abd Rashid, PhD Senior Lecturer

Faculty of Electrical Engineering Universiti Teknologi MARA Malaysia

(Member)

ROBIAH BINTI YUNUS, PhD Professor and Dean

School of Graduate Studies

Universiti Putra Malaysia

Date:

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Declaration by graduate student

I hereby confirm that:

� this thesis is my original work;

� quotations, illustrations and citations have been duly referenced;

� this thesis has not been submitted previously or concurrently for any other degree

at any institutions;

� intellectual property from the thesis and copyright of thesis are fully-owned by

Universiti Putra Malaysia, as according to the Universiti Putra Malaysia

(Research) Rules 2012;

� written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and innovation) before thesis is published (in the

form of written, printed or in electronic form) including books, journals,

modules, proceedings, popular writings, seminar papers, manuscripts, posters,

reports, lecture notes, learning modules or any other materials as stated in the

Universiti Putra Malaysia (Research) Rules 2012;

� there is no plagiarism or data falsification/fabrication in the thesis, and scholarly

integrity is upheld as according to the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia

(Research) Rules 2012. The thesis has undergone plagiarism detection software

Signature: Date:

Name and Matric No: Noor Hafizah Binti Abdul Aziz, GS36859

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Declaration by Members of Supervisory Committee

This is to confirm that:

� the research conducted and the writing of this thesis was under our

supervision;

� supervision responsibilities as stated in the Universiti Putra Malaysia

(Graduate Studies) Rules 2003 (Revision 2012-2013) were adhered to.

Signature:

Name of Chairman

of Supervisory

Committee:

Professor Dr. Raja Syamsul Azmir b. Raja Abdullah

Signature:

Name of Member

of Supervisory

Committee:

Dr. Fazirulhisyam b. Hashim

Signature:

Name of Member

of Supervisory

Committee:

Dr. Nur Emileen Abd Rashid

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TABLE OF CONTENTS

Page

ABSTRACT i

ABSTRAK iii

ACKNOWLEDGEMENTS v

APPROVAL vii

DECLARATION ix

LIST OF TABLES xiv

LIST OF FIGURES xvi

LIST OF APPENDICES LIST OF ABBREVIATIONS

xxiv

xxv

CHAPTER

1 INTRODUCTION 1

1.1 Background 1 1.2 Problem Statement and Research Opportunity 5 1.3 Aim and Objectives 5 1.4 Thesis Contributions 5 1.5 Thesis Outline 6

2 LITERATURE REVIEW 8 2.1 Introduction 8 2.2 Radar Technology 8 2.3 Forward Scattering Radar 10

2.3.1 Forward Scattering Radar Equation 11 2.3.2 Forward Scattering Radar Resolution 15 2.3.3 Forward Scattering Radar Cross Section 16 2.3.4 Doppler Signature 20

2.4 Passive Forward Scatter Radar Development 22 2.4.1 Passive Radar with Illuminator Sources 23 2.4.2 LTE as Passive Radar 25

2.5 LTE Technology 26 2.5.1 LTE Channel Structure 27 2.5.2 LTE Supported Frequency Bands 28 2.5.3 LTE Current and Future Deployment 29 2.5.4 LTE Propagation Characteristics 32

2.6 Pattern Classification 34 2.6.1 Principal Component Analysis 34

2.6.1.1 Training and Testing 38 2.6.2 K-Nearest Neighbour 39

2.7 Summary 40

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3 METHODOLOGY - EXPERIMENT SETUP AND DATA COLLECTION OF LTE BASED PASSIVE FORWARD SCATTERING RADAR 41 3.1 Introduction 41 3.2 Forward Scatter Radar Cross Section for Different Targets 41 3.3 LTE Coverage 46 3.4 Experiment Site 48

3.4.1 LTE Signal Propagation 50 3.5 Passive Forward Scatter Radar Experiment Setup 54

3.5.1 Low Frequency Circuit 56 3.5.2 Hardware Description 61 3.5.3 Recording Data Format 69 3.5.4 Data Collection 71

3.6 Vehicle Signature in Passive Forward Scattering Radar 76 3.7 Summary 84

4 DETECTION AND CLASSIFICATION RESULTS 85 4.1 Introduction 85 4.2 Detection 85

4.2.1 Power Spectral Density as a Features 90 4.3 Classification 108

4.3.1 Training and Testing 109 4.3.2 An Overview of the Classification System 110

4.3.2.1 Analysis in Various Baseline Crossing Range 120 4.3.2.2 Analysis in Various Target Speed 123

4.3.3 Real Case Scenario: Unknown Target Speed and

Unknown Target Baseline Crossing Range 126 4.3.4 Vehicle Classification with Fixed Target Speed and

Fixed Target Baseline Crossing Range 127 4.3.4.1 Target Speed of 5 km/h and Target Baseline

Crossing Range of 5 m 127 4.3.4.2 Target Speed of 5 km/h and Target Baseline








Crossing Range of 20 m 136

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4.3.4.10 Target Speed of 30 km/h and Target Baseline



Crossing Range of 20 m 139 4.3.4.13 Overall Classification Performance in Various

Vehicle with Fixed Target Speed and Fixed

Target Baseline Crossing Range 140 4.3.4.14 Simulation on Higher Target Speed of 60

km/h and 40 km/ with oversampling technique 146 4.3.5 Vehicle Classification Performance at Different Target

Speed 152 4.3.6 Vehicle Classification Performance at Different Target

Baseline Crossing Range 156 4.4 Summary 157

5 CONCLUSION AND FUTURE WORK 158

REFERENCES 160 APPENDICES 170 BIODATA OF STUDENT 247 LIST OF PUBLICATIONS 248

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LIST OF TABLES Table Page 1 Examples of illuminator of opportunity in passive radar application 24

2 Examples of illuminator of opportunity in passive FSR application 24

3 FDD LTE bands and frequencies [79] 29

4 Specification of LTE for input setup of RCS simulation 43

5 Three types of ground moving target 44

6 RCS maximum for three types of ground moving target 46

7 Components of Doppler frequency filters and two stage amplifiers 60

8 Components of passive forward scatter radar system 68

9 Data collection of LTE based passive forward scattering radar for

vehicle detection and classification at MAEPS 75

10 Parameters of estimated target signature in passive forward scatter

radar system 78

11 Classification on target baseline crossing range of Compact car 121

12 Classification on target baseline crossing range of Saloon car 122

13 Classification on target baseline crossing range of SUV 122

14 Classification on target speed of Compact car 124

15 Classification on target speed of Saloon car 124

16 Classification on target speed of SUV car 125

17 Vehicle classification with unknown target speed and unknown

baseline crossing range 127

18 Vehicle classification with speed of 5 km/h and baseline crossing

range of 5 m 140


range of 10 m 141


range of 20 m 141

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range of 5 m 142


range of 10 m 142


range of 20 m 143


range of 5 m 143


range of 10 m 144


range of 20 m 144


range of 5 m 145


range of 10 m 145


range of 20 m 146


range of 5 m by oversampling technique 147


range of 5 m by oversampling technique 149


range of 5 m by downsampling technique 150


range of 5 m by downsampling technique 151

34 Vehicle classification with baseline crossing range of 5 m 154



37 Vehicle classification with target speed of 30 km/h 157

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LIST OF FIGURES

Figure Page

1.1 Possible application of LTE based passive FSR 4

2.1 Radar Systems: (a) Monostatic Radar (b) Bistatic Radar 8

2.2 Passive Bistatic Radar 9

2.3 Geometrical of (a) Forward Scatter Radar (b) Passive Forward Scatter

Radar 10

2.4 Typical forward scatter radar (FSR) topology 12

2.5 Range resolution in (a) bistatic and (b) FSR configuration [29] 15

2.6 Shadow field forming [27] 18

2.7 RCS and FSML width for spherical targets [45] 20

2.8 Doppler shift of a moving source 21

2.9 Passive bistatic radar system 22

2.10 Result of normalized cross ambiguity function for a target detection

in conventional passive bistatic radar (a) the mesh plot (b) the contour

plot [75] 26

2.11 Mobile Frequencies in Malaysia [76] 27

2.12 LTE Coverage by Country [80] 30

2.13 LTE Coverage in Malaysia [82] 31

2.14 4G LTE Coverage in Peninsular Malaysia [84] 32

2.15 Multipath propagation 33

2.16 General scheme of pattern classification system 34

2.17 Block diagram of vehicle classification in LTE based passive forward

scattering radar system 35

2.18 The variance and cumulative variance described by a given principal

component [96] 37

2.19 The training data position in the PCA space [96] 38

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2.20 Distance between test data and training data using k =3 [96] 40

3.1 Block diagram for complex shaped radar cross section. 42

3.2 Modeling a target with top view, side view, front view and three

dimensional view in AutoDesk. 42

3.3 RCS of (a) Compact (b) Saloon and (c) SUV in CST 45

3.4 4G LTE coverage in Peninsular Malaysia [84] 47

3.5 The tower of LTE network in Kuala Lumpur [84] 47

3.6 LTE coverage and towers in MAEPS area [84] 48

3.7 Geographical area of the experiment site in MAEPS 49

3.8 Schematic of the LTE based passive forward scattering radar system

in MAEPS 49

3.9 The received LTE signal spectrum at MAEPS with center frequency

of 2.63 GHz and bandwidth of 20 MHz 50

3.10 LTE transmission with ground reflection 51

3.11 The field experiment of LTE based passive forward scatter radar 53

3.12 LTE signal propagation with Friis equation and ground existence due

to polarization 54

3.13 Architecture of LTE based passive forward scattering radar receiver

for target detection 55

3.14 Low pass filter circuit 56

3.15 LPF frequency response 57

3.16 High pass filter circuit 58

3.17 HPF frequency response 58

3.18 Amplifier circuit 59

3.19 Doppler frequency filters and amplifiers circuit 60

3.20 Physical PCB of Doppler frequency filters and two stage amplifiers 60

3.21 Hardware description of LTE based passive forward scattering radar 61

3.22 Field experiment of LTE based passive forward scattering radar at

MAEPS 62

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3.23 Antenna receiver configuration 63

3.24 Components for ground moving target detection 63

3.25 Horn antenna receiver at field experiment 64

3.26 Outline of horn antenna [112] 65

3.27 Low noise amplifier 65

3.28 Tunable bandpass filter 66

3.29 Doppler extraction by diode detector and LPF 66

3.30 Low pass filter 67

3.31 The implemented experiment of a passive forward scatter radar

receiver for detection 69

3.32 Flowchart of recording data from ADC 70

3.33 Arrangement of cones for moving target trajectory 71

3.34 Schematic of ground moving target trajectory at MAEPS 72

3.35 GPS tracker for location and speed range of vehicle while moving 73

3.36 Simulated signal of Compact car with speed of 10 km/h and baseline

of 10 meter 79

3.37 Simulated signal of Saloon car with speed of 10 km/h and baseline of

10 meter 80

3.38 Simulated signal of SUV car with speed of 10 km/h and baseline of

10 meter 80

3.39 Simulated signal of Compact car with speed of 30 km/h 81

3.40 Simulated signal of Saloon car with speed of 30 km/h 82

3.41 Simulated signal of SUV car with speed of 30 km/h 82

3.42 Comparison between the simulated and measured signals for (a)

Compact, (b) Saloon and (c) SUV cars 83

4.1 Signal processing of vehicle detection and classification in passive

forward scattering radar 85

4.2 Raw signal of Saloon car with speed of 20 km/h and distance of 5 m 86

4.3 Selected signal of Saloon car with 4 seconds 87

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4.4 Segmented signal of Saloon car 88

4.5 Raw signal of vehicles with segmentation process 89

4.6 Denoised signal of vehicles with speed of 20 km/h and range of 5 m 90

4.7 Divison of a ground moving target into strips of equal width 91

4.8 PSD of vehicles with speed of 20 km/h and range of 5 m 93

4.9 Feature extraction of Compact car in time domain (left) and power

spectrum (right) with fixed target baseline crossing of 5 meters at

different speeds (a) 5 km/h (b) 10 km/h (c) 20 km/h and (d) 30 km/h 94

4.10 Simulated signal of Compact car in time domain (left) and power









4.13 Feature extraction of Saloon car in time domain (left) and power









4.16 Feature extraction of SUV car in time domain (left) and power









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4.19 Division in classification system 110

4.20 The vehicle classification sytem 111

4.21 PCA space of Compact car with target baseline crossing 5 m (a)

training data (b) testing data 112

4.22 The distances using k-NN with 3 nearest neighbours to ‘X’ (unknown vehicle feature vector) and ‘О’ (three nearest neighbours) 113


training data and (b) testing data 114


training data (b) testing data 114

4.25 Variance explained of training data for Compact car with baseline

crossing of (a) 5 m, (b) 10 m and (c) 20 m 115

4.26 PCA space of Compact car with speed of 5 km/h (a) training data (b)

testing data 117


testing data 117


testing data 117


testing data 118

4.30 Variance explained of training data for Compact car with speed of

(a) 5 km/h (b) 10 km/h (c) 20 km/h and (d) 30 km/h 119

4.31 PCA space with three PCs for Compact car with speed of (a) 5 km/h

(b) 10 km/h (c) 20 km/h and (d) 30 km/h 120

4.32 Graph of classification performance of target baseline crossing range

with specific target 123

4.33 Graph of classification performance of target speed with specific

target 125

4.34 PCA space of vehicle category with unknown speed and baseline

crossing point (a) training data and (b) testing data 126

4.35 Vehicles feature extraction in time domain (left) and power spectrum

(right) with target speed of 5 km/h and target baseline crossing of 5

meters 127

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4.36 PCA space of vehicle category with target speed of 5 km/h and target

baseline crossing range of 5 m (a) training data and (b) testing data 128



metera 128





meters 130





meters 131

4.42 PCA space of vehicle category with target speed of 10 km/h and

target baseline crossing range of 5 m (a) training data and (b) testing

data 131



meters 132



data 132



meters 133



data 133



meters 134



data 134

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meters 135



data 135



meters 136



data 136



meters 137



data 137



meters 138



data 138



meters 139



data 139

4.59 Vehicles feature extraction by oversampling technique in time

domain (left) and power spectrum (right) with target speed of 60 km/h

and target baseline crossing of 5 meters 146


target baseline crossing range of 5 m by oversampling technique (a)


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4.61 Vehicles feature extraction by oversampling technique in time




target baseline crossing range of 5 m by oversampling technique (a)


4.63 Vehicles feature extraction by downsampling technique in time




target baseline crossing range of 5 m by downsampling technique (a)


4.65 Vehicles feature extraction by downsampling technique in time




target baseline crossing range of 5 m by downsampling technique (a)


4.67 Graph of classification performance in target speed (oversampling

and downsampling techniques) 152

4.68 PCA space of vehicle category with target baseline crossing range of

5 m (a) training data and (b) testing data 153





4.71 PCA space of vehicle category with target speed of 30 km/h (a)


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LIST OF APPENDICES

Appendix Page

A Specifications of the Components in Passive Forward Scattering

Radar Based on LTE Signal

170

B Schematic of Printed Circuit Board 188

C Real View of Vehicle 189

D Performance Results on Other Vehicles 191

E Vehicle Performance through Variance Explained 201

F Images of MAEPS 204

G FSR Power Budget 205

H Publications 206

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LIST OF ABBREVIATIONS

3GPP 3rd Generation Partnership Project

ACK Acknowledgement

ADC Analog to Digital Converter

AF Ambiguity Function

ANN Artificial Neural Network

ARQ Automatic Repeat Query

ASR Aircraft Security Radar

BFSC Bistatic Forward Scattering Radar

BPF Band Pass Filter

BTS Base Transceiver Station

CDMA Code Division Multiple Access

CFI Control Format Indicator

CP Cyclic Prefix

CPF Complex Profile Function

CRC Cyclic Redundancy Check

CSRS Cell Specific Reference Signals

CST Computer Simulation Technology

DAB Digital Audio Broadcasting

DC Direct Current

DCI Downlink Control Information

DVB Digital Video Broadcasting

DVB-T Digital Video Broadcasting-Terrestrial

EDGE Enhanced Data rates for GSM Evolution

EM Electromagnetic

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eNB evolved NodeB

FDD Frequency Division Duplex

FFT Fast Fourier transform

FM Frequency Modulation

FR4 Flame Retardant 4

FS Forward Scatter

FSCS Forward Scatter Cross Section

FSML Forward Scatter Main Lobe

FSR Forward Scattering Radar

GMTI Ground Moving Target Indication

GNSS Global Navigation Satellites Systems

GP Guard Period

GPS Global Positioning System

GSM Global Systems for Mobile communications

HPF High Pass Filter

HSPA High Speed Packet Access

IEEE Institute of Electrical Electronics Engineers

ISAR Inverse Synthetic Aperture Radar

J-FET Junction – Field Effect Transistor

k-NN k-Nearest Neighbours

LEO Low Earth Orbit

LNA Low Noise Amplifier

LOS Line of Sight

LPF Low Pass Filter

LTE Long-Term Evolution

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MAEPS Malaysia Agro Exposition Park Serdang

MCMC Malaysian Communications and Multimedia Commission

MDV Minimum Detectable Velocity

MIB Master Information Block

MIMO Multiple Input and Multiple Output

MIMO-OFDM Multiple Input and Multiple Output–Orthogonal Frequency

Division Multiplexing

MRCS Monostatic Radar Cross Section

NACK Non-Acknowledgement

NLOS Non Line of Sight

OFDM Orthogonal Frequency Division Multiplex

OFDMA Orthogonal Frequency Division Multiple Access

PBCH Physical Broadcast Channel

PBR Passive Bistatic Radar

PC Principal Component

PCA Principal Component Analysis

PCB Printed Circuit Board

PRB Physical Resource Block

PSD Power Spectrum Density

PSS Primary Synchronization Signal

QPSK Quadrature Phase Shift Keying

RADAR Radio Detection and Ranging

RB Resource Block

RCS Radar Cross Section

RHS Radio Holographic Signal

SAR Synthetic Aperture Radar

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SISAR Shadow Inverse Synthetic Aperture Radar

SLR Signal to Leakage Ratio

SMS Short Messaging Services

SNR Signal to Noise Ratio

SSS Secondary Synchronization Signal

SUV Sport Utility Vehicle

TDD Time Division Duplex

TRP Two-Ray Path

UAV Unmanned Aerial Vehicle

UeSRS User equipment Specific Reference Signals

UMTS Universal Mobile Telecommunications System

UPM Universiti Putra Malaysia

UpPTS Uplink Pilot Time Slot

UWB Ultra Wideband

VHF Very High Frequency

VHF Very High Frequency

VSWR Voltage Standing Wave Ratio

Wi-Fi Wireless Fidelity

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

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CHAPTER 1

1 INTRODUCTION

1.1 Background

Passive bistatic radar (PBR) system can utilize the illuminator of opportunity to

improve radar proficiency. By exploiting the forward scattering technique and

procedure into the specific mode of passive bistatic radar, the target detection and

classification in passive radar can be enhanced. This system is identified as the

passive bistatic Forward Scattering Radar (FSR). The operational mode of the FSR

related to the bistatic angle is near 180⁰, where the desired radar signal is formed

through the shadowing of a direct signal by the target rather than back scattering

signal from the target such as in conventional radar system [1]. Among the

uniqueness of FSR features are enhanced target radar cross section, long coherent

intervals of the receiving signal, absence of signal oscillations and essentially simple

equipment and implementation [2], [3].

A passive bistatic radar system uses non-cooperative transmitter, which means it

exploits other external radiation source for target detection. Therefore, this

advantage makes passive radar remarkably cost effective due to the low expenses to

be paid for a transmitter system. The basis of non-emitting radio energy leads to the

system being practically camouflaged to surveillance. Until now, illuminators of

opportunity have been employed namely in Television Broadcasting [4], FM radio

[5], Digital Video Broadcasting (DVB) [6], Digital Audio Broadcasting (DAB) [6],

satellites [7], [8], Global Systems for Mobile communications (GSM) [9] and Long

Term Evolution (LTE) [10].

In conventional passive bistatic radar, the receiver circuit uses the heterodyne

concept that involves a reference signal from direct transmission for synchronization.

The received signal is then down converted to the base band and then the target

detection is evaluated by analyzing the ambiguity function of the received signal.

Alternatively, this thesis presents the FSR mode being integrated into a passive radar

system, which is valuable due to the passive FSR not requiring a reference signal

from the base station, and target detection scheme based on signal envelope is

straight forward without the complicated signal preprocessing step. In addition it

also enjoys the simple forward scatter receiver circuit and significant improvement

in radar cross section (RCS) at the forward scatter main lobe (FSML). The

motivating feature in passive forward scatter radar configuration is the sharp rise of

the forward scatter radar cross section (RCS), which increases the sensitivity of the

radar system.

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Current investigations organized by other researchers using the passive radar with

FSR mode are such as Global Positioning System (GPS) satellites signals and have

been demonstrated in [11]–[14] for air target detection and in [15] for ground target

detection. An investigation using GSM as a signal source and exploiting the forward

scatter geometry for target detection is explained in [16], by using two antennas for

reference and target’s receive signals. Latest publications on passive forward scatter radar using Digital Video Broadcasting-Terrestrial (DVB-T) signals have presented a

new algorithm for signal detection, which is for air target detection, whereby it could

detect an airplane [17].

Despite the research done so far, the literature is lacking in results for ground target

detection and no result was found for ground target classification in passive FSR

system. Therefore, the aim of this thesis is to show the feasibility of passive FSR for

ground moving target detection and classification. In addition, the target’s distance from the radar sensor can also be determined. The evaluation was carried out on a

real Long Term Evolution (LTE) signal transmitted through air. Three vehicles of

different sizes and speeds were tested. The three categories of cars that were used

and tested were namely Compact, Saloon and Sport Utility Vehicle (SUV). The

passive FSR experiment results show that the vehicles were successfully detected,

even by unprocessed received signals without any complicated signal processing

techniques, and the proposed classification system provided results on classification.

The results from this thesis can enhance the passive radar system’s capability by integrating FSR mode in the conventional PBR system.

The LTE has special characteristics that makes it attractive to be used as an

illuminator of opportunity for passive radar applications. Among the characteristics

of the LTE signal are it has a broad bandwidth range, from 1.4 MHz to 20 MHz,

which provides a high range resolution in comparison to other illuminators of

opportunity, and the variety of allocated frequency bands in the range of 800 MHz

until 3.5 GHz allowing the LTE to be deployed in many countries, is significant for

the future broad coverage of LTE worldwide [18]. Additionally, the number of

commercial LTE networks is seeing a massive increase year by year. For that reason,

the growth of LTE signal availability will increase LTE based passive radars’ deployment opportunities. Moreover, analysis on the LTE signal Ambiguity

Function, (AF) which employs the orthogonal frequency division multiple access

(OFDMA), shows that low side lobes and high range resolution can be achieved

[19]. Thus, the LTE signal characteristics, among others, inspired this research to use

the LTE signal as an illuminator of opportunity for passive FSR system, especially

for ground moving target detection and classification.

Moreover, FSR performance does not depend on the LTE signal modulation and

consequently does not interrupt the detection and classification of ground moving

target in LTE based passive forward scattering radar system. This emphasizes the

capability of FSR to detect ground moving target through monitoring perturbations

in the direct path of LTE signal.

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Furthermore, this thesis shows the first classification result on ground moving target

in passive forward scatter radar system. The power spectral density of the vehicle

signal is used as the target signature and input of the classification system. The

frequency spectrum is extracted for feature selection, which has useful information

of different targets. The difference in spectrum is referred to the frequency domain

signature that is unique for each ground moving target’s silhouette and target

forward scatter radar cross section that is useful for target classification. The feature

selection have high dimensions where PCA is employed to reduce the dimensionality

of the spectral feature vectors inherently clustering and classifying the data by

exploiting the correlation between the features. The great potential in the passive

forward scatter radar system provides a new research area in passive radar that can

be used for diverse remote monitoring, including border protection, microwave

fences, building monitoring, traffic surveillance, and so forth. Figure 1.1 illustrates

several possible application of the LTE based passive FSR.

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1.2 Problem Statement and Research Opportunity

The main challenges faced by conventional passive radar are:

i. Conventional passive radar system uses two channels for synchronisation

between direct signal and reflected signal. It then uses the difference between

the two channels for target detection and target range. Having two channels

at the receiver will give implication to extra cost. In addition, the requirement

for signal synchronisation complicates the signal processing.

ii. The received signal from illuminator of opportunity have varies signal

modulation characteristic that introduce more ambiguities in conventional

passive radar processing which affects target detection. Furthermore,

modulation in the signal affects classification complexity due to the

requirement of many databases for each signal modulation type.

iii. Radar system for air target is designed for high altitude targets and long

range, making low altitude targets undetectable in the shadow region of that

radar system. So, low altitude coverage is required to detect targets moving

near to the ground closed to the radar system such as the ground moving

threats or any low altitude targets through the shadowing of a direct signal by

the target shape.

1.3 Aim and Objectives

The aim of this thesis is to develop an LTE based passive forward scatter radar

system for ground moving target detection and classification. The following

objectives have been set in order to fulfil this aim:

i. To investigate the feasibility of forward scatter configuration mode using

LTE signal as an innovative illuminator of opportunity in passive radar

system for ground moving targets detection.

ii. To develop experimental LTE based passive forward scatter radar system

receiver and organize site experiments for ground moving target detection

and classification.

iii. To find the unique features of Doppler frequency for ground moving targets

from the LTE based passive forward scatter radar system for classifier.

1.4 Thesis Contributions

The specific contributions of this thesis that focuses on developing LTE based

passive forward scatter radar system for ground moving target detection and

classification are listed in the respective chapters throughout the thesis. The main

contributions of this thesis are presented:

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i. Proof of concept by real experiment data of a passive forward scatter radar

based on LTE signal for vehicle (ground target) detection and classification.

ii. The selection of good features of the received signal that represent vehicle

signature to be used for vehicle classification.

iii. Vehicle classification system and algorithm in passive forward scatter radar

based on LTE signal.

iv. Prediction system of vehicle’s speed and baseline crossing point in passive

forward scatter radar based on LTE signal.

In this research work, there is several limitation, which are:

i. One transmitter is used from the LTE base station in passive forward scatter

radar system. This is a limitation to the system since LTE signal is the first to

be investigated in forward scatter configuration to detect the ground moving

target.

ii. Three ground moving targets which are Compact, Saloon and SUV, are used

for detection and classification, and are common vehicles in Malaysia with

different size and shape.

iii. The distance between the LTE transmitter and passive radar receiver is

limited to 450 meters because the experiment work is carried out in a parking

space, where it has a suitable geometrical area, good coverage of LTE signal

and the point of receiver to transmitter is in direct line-of-sight.

1.5 Thesis Outline

The layout of this thesis is organized as follows:

Chapter 2 briefly discusses the radar configuration that indicates passive forward

scatter radar system. This chapter emphases on the target detection technique in

passive FSR by exploiting the enhancement of the target’s RCS. The fundamental

received signal equation is derived and the proposed passive FSR system is also

explained. The passive forward scatter radar development are discussed on

illuminator sources for passive radar, which includes WiMAX, GSM, GPS and LTE.

The specifics of LTE are also presented, which contains channel, frame structure,

downlink channel and signal of LTE, current and future development of LTE and

LTE propagation characteristics. At the end of this chapter, pattern classification for

the target detection using LTE based passive forward scatter radar that emphasize on

Principal Component Analysis (PCA) and k-Nearest Neighbours (k-NN) algorithm is

discussed.

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Chapter 3 elaborates on the LTE based passive forward scattering radar architecture

at the experiment site. The experiment data collection is briefly discussed on the

experiment setup, hardware description, circuit of Doppler frequency filter and

amplifier, recording data format, data collection method and target description.

Subsequently, the chapter goes into detail on the experimental data description,

which comprises simulation target and measured signal.

Chapter 4 explains the experiment results obtained from an LTE based passive

forward scattering radar at MAEPS to perform the vehicle feature extraction and

classification. The feature extraction involves data selection, segmentation, denoise

and transformation into power spectral density (PSD). The explanation of the raw

data signal processing for vehicle classification in passive forward scattering radar is

deliberated in details in this chapter. Next, discusses the classification system

commencing on training and testing data, and overview of the classification system

for target classification by using PCA. This chapter also concentrates on

classification performance for various target baseline crossing range and

classification performance for various target speed for each type of vehicle. Next, it

describes classification performance for various vehicle with fixed target speed and

fixed target baseline crossing range. The ultimate classification performance is from

the accumulation of various target speed and from the accumulation of various target

baseline crossing range, which positively resulted in distinguishing the vehicles in

the three categories (Compact, Saloon and SUV).

Chapter 5 summarizes and concludes the research based on the results presented in

this thesis. The conclusion and direction for future work are outlined in the last

section.

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6 REFERENCES

[1] J. Glaser, “Forward Scatter Radar for Future Systems,” WSTIAC Q., vol. 10,

no. 3, pp. 3–8, 2011.

[2] M. Cherniakov, Bistatic Radars: Emerging Technology. John Wiley & Sons

Ltd, 2008.

[3] M. Cherniakov, R. S. A. R. Abdullah, P. Jančovič, M. Salous, and V. Chapursky, “Automatic ground target classification using forward scattering

radar,” IEE Proc. - Radar, Sonar Navig., vol. 153, no. 5, p. 427, Oct. 2006.

[4] H. D. Griffiths and N. R. W. Long, “Television-based bistatic radar,” in IEE Proceedings F Communications, Radar and Signal Processing, 1986, vol.

133, no. 7, pp. 649–657.

[5] P. E. Howland, D. Maksimiuk, and G. Reitsma, “FM radio based bistatic radar,” in IEE Proceedings - Radar, Sonar and Navigation, 2005, vol. 152,

no. 3, pp. 107–115.

[6] D. Poullin, “Passive detection using digital broadcasters (DAB, DVB) with COFDM modulation,” in IEE Proceedings - Radar, Sonar and Navigation,

2005, vol. 152, no. 3, pp. 143 – 152.

[7] J.-L. Bárcena-Humanes, P.-J. Gómez-Hoyo, M.-P. Jarabo-Amores, D. Mata-

Moya, and N. Del-Rey-Maestre, “Feasibility Study of EO SARs as Opportunity Illuminators in Passive Radars: PAZ-Based Case Study.,” Sensors (Basel)., vol. 15, no. 11, pp. 29079–29106, Jan. 2015.

[8] H. D. Griffiths, A. J. Garnett, C. J. Baker, and S. Keaveney, “Bistatic radar using satellite-borne illuminators of opportunity,” in Radar 92. International Conference, 1992, pp. 276–279.

[9] H. Sun, D. K. P. Tan, and Y. Lu, “Aircraft target measurements using A GSM-based passive radar,” in 2008 IEEE Radar Conference, 2008, pp. 1–6.

[10] R. S. A. Raja Abdullah, F. H. Hashim, A. A. Salah, N. E. Abdul Rashid, A.

Ismail, and N. H. Abdul Aziz, “Experimental investigation on target detection and tracking in passive radar using long-term evolution signal,” IET Radar, Sonar Navig., vol. 10, no. 3, pp. 577–585, Mar. 2016.

[11] I. Suberviola, I. Mayordomo, and J. Mendizabal, “Experimental Results of Air Target Detection With a GPS Forward-Scattering Radar,” IEEE Geosci. Remote Sens. Lett., vol. 9, no. 1, pp. 47–51, Jan. 2012.

[12] V. Behar and C. Kabakchiev, “Detectability of Air Target Detection using Bistatic Radar Based on GPS L5 Signals,” in Proceedings IRS 2011, 2011, pp.

212–217.

© COPYRIG

HT UPM

161

[13] V. Koch and R. Westphal, “A new approach to a multistatic passive sensor for air defense,” Radar Conference, Record of the IEEE 1995 International. IEEE, pp. 22–28, 1995.

[14] C. Clemente and J. J. Soraghan, “Passive bistatic radar for helicopters classification: A feasibility study,” Radar Conference (RADAR). IEEE, pp.

946–949, 2012.

[15] C. Kabakchiev, I. Garvanov, V. Behar, P. Daskalov, and H. Rohling, “Study of moving target shadows using passive Forward Scatter radar systems,” Radar Symposium (IRS), 2014 15th International. pp. 1–4, 2014.

[16] P. Krysik, K. Kulpa, and P. Samczynski, “GSM based passive receiver using forward scatter radar geometry,” Radar Symposium (IRS), 2013 14th International, vol. 2. pp. 637–642, 2013.

[17] M. Marra, A. De Luca, S. Hristov, L. Daniel, M. Gashinova, and M.

Cherniakov, “New algorithm for signal detection in passive FSR,” in 2015 IEEE Radar Conference, 2015, pp. 218–223.

[18] A. A. Salah, R. S. A. Raja Abdullah, A. Ismail, F. Hashim, C. Y. Leow, M. B.

Roslee, and N. E. A. Rashid, “Feasibility study of LTE signal as a new illuminators of opportunity for passive radar applications,” in RF and Microwave Conference (RFM), 2013 IEEE International, 2013, pp. 258–262.

[19] S. Ahmadi, “An overview of 3GPP long-term evolution radio access

network,” New Directions in Wireless Communications Research. Springer

US, pp. 431–465, 2009.

[20] M. I. Skolnik, Radar Handbook. New York: McGraw-Hill Book Company,

1970.

[21] L. Liu, M. Popescu, M. Skubic, M. Rantz, T. Yardibi, and P. Cuddihy,

“Automatic fall detection based on Doppler radar motion signature,” in 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 2011, pp. 222–225.

[22] N. J. Willis, Bistatic Radar, vol. 8. Technology Service Corporation, 1995.

[23] A. A. Salah, R. S. A. Raja Abdullah, A. Ismail, F. Hashim, and N. H. Abdul

Aziz, “Experimental study of LTE signals as illuminators of opportunity for

passive bistatic radar applications,” Electron. Lett., vol. 50, no. 7, pp. 545–547, Mar. 2014.

[24] M. I. Skolnik, Introduction to radar systems. New York: McGraw-Hill Book

Company, 1980.

[25] J. Glaser, “Bistatic RCS of Complex Objects Near Forward Scatter,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-21, no. 1, pp. 70–78, Jan. 1985.

© COPYRIG

HT UPM

162

[26] J. Glaser, “Some results in the bistatic radar cross section (RCS) of complex objects,” Proc. IEEE, vol. 77, no. 5, pp. 639–648, May 1989.

[27] M. Cherniakov, Bistatic radar: principles and practice. John Wiley & Sons,

2007.

[28] M. Gashinova, M. Cherniakov, L. Daniel, V. Sizov, and E. Hoare,

“Phenomenology of Doppler forward scatter radar for surface targets observation,” IET Radar, Sonar Navig., vol. 7, no. 4, pp. 422–432, Apr. 2013.

[29] M. Gashinova, L. Daniel, E. Hoare, V. Sizov, K. Kabakchiev, and M.

Cherniakov, “Signal characterisation and processing in the forward scatter mode of bistatic passive coherent location systems,” EURASIP J. Adv. Signal Process., vol. 2013, no. 1, p. 36, 2013.

[30] T. Long, C. Hu, and C. Mikhail, “Ground moving target signal model and power calculation in forward scattering micro radar,” Sci. China Ser. F Inf. Sci., vol. 52, no. 9, pp. 1704–1714, Sep. 2009.

[31] T. Zeng, C. Hu, M. Cherniakov, C. Zhuo, and C. Mao, “Joint parameter estimation and Cramer-Rao bound analysis in ground-based forward scatter

radar,” EURASIP J. Adv. Signal Process., vol. 2012, no. 1, p. 80, 2012.

[32] C. Hu, V. Sizov, M. Antoniou, M. Gashinova, and M. Cherniakov, “Optimal Signal Processing in Ground-Based Forward Scatter Micro Radars,” IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 4, pp. 3006–3026, Oct. 2012.

[33] D. Schiavulli, F. Nunziata, M. Migliaccio, F. Frappart, G. Ramilien, and J.

Darrozes, “Reconstruction of the Radar Image From Actual DDMs Collected by TechDemoSat-1 GNSS-R Mission,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., pp. 1–9, 2016.

[34] M. Antoniou, V. Sizov, P. Jancovic, R. Abdullah, N. E. A. Rashid, and M.

Cherniakov, “The concept of a forward scattering micro-sensors radar

network for situational awareness,” in 2008 International Conference on Radar, 2008, pp. 171–176.

[35] A. B. Blyakhman, A. G. Ryndyk, and S. B. Sidorov, “Forward scattering radar moving object coordinate measurement,” in Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037], 2000, pp. 678–682.

[36] V. V. Chapurskiy, “Image construction from one-dimensional radioholograms

synthesized at small angles of diffraction,” Radiotekhnika i Elektron. (ISSN 0033-8494), vol. 33, pp. 1747–1756, 1988.

[37] B. S. Surikov, E. A. Khasina, and V. V. Chapurskiy, “Correlation and spectral functions of one-dimensional radio holograms synthesized at small diffraction

angles,” Radiotekhnika i Elektron. (ISSN 0033-8494), vol. 34, pp. 409–419,

1989.

© COPYRIG

HT UPM

163

[38] V. V. Chapurskiy and V. N. Sablin, “SISAR: shadow inverse synthetic aperture radiolocation,” in Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037], 2000, pp. 322–328.

[39] N. Li, T. Zhang, X. Lv, and S. Zhang, “An Improved Joint Time Frequency Approach for Shadow Image of Bistatic Forward Scattering Radar,” in 2006 CIE International Conference on Radar, 2006, pp. 1–4.

[40] T. Zeng, X. Li, C. Hu, and T. Long, “Investigation on accurate signal

modelling and imaging of the moving target in ground-based forward scatter

radar,” IET Radar, Sonar Navig., vol. 5, no. 8, p. 862, Oct. 2011.

[41] C. Hu, X. Li, T. Long, and C. Zhou, “An accurate SISAR imaging method of ground moving target in forward scatter radar,” Sci. China Inf. Sci., vol. 55,

no. 10, pp. 2269–2280, May 2012.

[42] L. Changjiang, H. Cheng, X. Jia, and L. Teng, “Modified signal modeling and imaging method of non-perpendicular crossing targets in forward scatter

radar,” in 2014 IEEE Radar Conference, 2014, pp. 0291–0295.

[43] C. Hu, C. Liu, R. Wang, and T. Zeng, “Improved Reconstruction of Radio Holographic Signal for Forward Scatter Radar Imaging.,” Sensors (Basel)., vol. 16, no. 5, p. 651, Jan. 2016.

[44] M. A. Richards, W. A. Holm, and J. A. Scheer, Principles of Modern Radar: Basic Principles. N. J.: SciTech Publishing Inc., 2009.

[45] G. W. Stimson, Introduction to Airborne Radar. El Segundo, California:

Hughes Aircraft Company, 1983.

[46] D. K. Barton, Modern radar system analysis. Norwood, MA: Artech House,

1988.

[47] V. Sizov, M. Cherniakov, and M. Antoniou, “Forward scattering radar power budget analysis for ground targets,” IET Radar Sonar …, 2007.

[48] M. Cherniakov, M. Gashinova, C. Hu, M. Antoniou, V. Sizov, and L. Y.

Daniel, “Ultra wideband forward scattering radar: Concept and prospective,” in International Conference on Radar Systems, 2007, pp. 1–5.

[49] S. W. Lee and R. Mittra, “Fourier transform of a polygonal shape function and its application in electromagnetics,” IEEE Trans. Antennas Propag., vol. 31,

no. 1, pp. 99–103, Jan. 1983.

[50] M. Gashinova, L. Daniel, K. Kabakchiev, V. Sizov, E. Hoare, and M.

Cherniakov, “Phenomenology of signals in FSR for surface targets detection,” in IET International Conference on Radar Systems (Radar 2012), 2012, pp.

1–6.

© COPYRIG

HT UPM

164

[51] L. Daniel, M. Gashinova, and M. Cherniakov, “Maritime Target Cross Section Estimation for an Ultra-Wideband Forward Scatter Radar Network,” in Radar Conference, 2008. EuRAD 2008. European, 2008, pp. 316–320.

[52] “Doppler Effect,” Wikimedia Foundation. [Online]. Available:

https://en.wikipedia.org/wiki/Doppler_effect. [Accessed: 19-May-2013].

[53] N. J. Willis and H. D. Griffiths, Advances in Bistatic Radar (Electromagnetics and Radar). SciTech Publishing Inc., 2007.

[54] P. Howland, “Target tracking using television-based bistatic radar,” in IEE Proceedings - Radar, Sonar and Navigation, 1999, vol. 146, no. 3, pp. 166–174.

[55] H. Guo, K. Woodbridge, and C. J. Baker, “Evaluation of WiFi beacon transmissions for wireless based passive radar,” in 2008 IEEE Radar Conference, 2008, pp. 1–6.

[56] Q. Wang, C. Hou, and Y. Lu, “WiMAX signal waveform analysis for passive radar application,” in 2009 International Radar Conference “Surveillance for a Safer World” (RADAR 2009), 2009, pp. 1–6.

[57] H. D. Griffiths and C. J. Baker, “Passive coherent location radar systems. Part 1: Performance prediction,” in IEE Proceedings - Radar, Sonar and Navigation, 2005, vol. 152, no. 3, pp. 153–159.

[58] C. J. Baker, H. D. Griffiths, and I. Papoutsis, “Passive coherent location radar systems. Part 2: Waveform properties,” IEE Proc. - Radar, Sonar Navig., vol.

152, no. 3, pp. 160–168, Jun. 2005.

[59] H. Kuschel and D. O’Hagan, “Passive radar from history to future,” in 11-th International Radar Symposium, 2010, pp. 1–4.

[60] K. Kabakchiev, L. Y. Daniel, V. Sizov, E. Hoare, M. Gashinova, and M.

Cherniakov, “Received signal characterization in forward scatter radar for maritime application,” in 2011 12th International Radar Symposium (IRS),2011, pp. 67–72.

[61] D. Poullin and M. Flecheux, “Recent progress in Passive Coherent Location (PCL) concepts and technique in France using DAB or FM broadcasters,” in 2008 IEEE Radar Conference, 2008, pp. 1–5.

[62] R. Saini and M. Cherniakov, “DTV signal ambiguity function analysis for

radar application,” in IEE Proceedings - Radar, Sonar and Navigation, 2005,

vol. 152, no. 3, pp. 133–142.

[63] M. Cherniakov, D. Nezlin, and K. Kubik, “Air target detection via bistatic radar based on LEOS communication signals,” in IEE Proceedings - Radar, Sonar and Navigation, 2002, vol. 149, no. 1, p. 33.

© COPYRIG

HT UPM

165

[64] D. K. P. Tan, H. Sun, Y. Lu, M. Lesturgie, and H. L. Chan, “Passive radar using Global System for Mobile communication signal: theory,

implementation and measurements,” in IEE Proceedings - Radar, Sonar and Navigation, 2005, vol. 152, no. 3, p. 116.

[65] F. Colone, K. Woodbridge, H. Guo, D. Mason, and C. J. Baker, “Ambiguity Function Analysis of Wireless LAN Transmissions for Passive Radar,” in IEEE Transactions on Aerospace and Electronic Systems, 2011, vol. 47, no.

1, pp. 240–264.

[66] R. Saini, R. Zuo, and M. Cherniakov, “Development of space-surface bistatic

synthetic aperture radar with GNSS trasmnitter of opportunity,” in 2008 IEEE Radar Conference, 2008, pp. 1–6.

[67] M. Cherniakov, R. Saini, R. Zuo, and M. Antoniou, “Space Surface Bistatic SAR with Space-Borne Non-Cooperative Transmitters,” in European Radar Conference, 2005. EURAD 2005., 2005, pp. 9–12.

[68] I. Walterscheid, T. Espeter, A. R. Brenner, J. Klare, J. H. G. Ender, H. Nies,

R. Wang, and O. Loffeld, “Bistatic SAR Experiments With PAMIR and TerraSAR-X—Setup, Processing, and Image Results,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 8, pp. 3268–3279, Aug. 2010.

[69] L. Cazzani, C. Colesanti, D. Leva, G. Nesti, C. Prati, F. Rocca, and D. Tarchi,

“A ground-based parasitic SAR experiment,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2132–2141, 2000.

[70] R. S. A. Raja Abdullah, A. A. Salah, A. Ismail, F. Hashim, N. E. A. Rashid,

and N. H. Abdul Aziz, “LTE-Based Passive Bistatic Radar System for

Detection of Ground-Moving Targets,” ETRI J., vol. 38, no. 2, pp. 302–313,

2016.

[71] “LTE Encyclopedia,” 3GPP LTE Encyclopedia. [Online]. Available:

https://sites.google.com/site/lteencyclopedia/home. [Accessed: 26-May-2014].

[72] “Long Term Evolution (LTE): A Technical Overview,” Motorola. [Online].

Available: http://www.motorolasolutions.com/. [Accessed: 20-May-2015].

[73] “LTE (telecommunication),” Wikimedia Foundation. [Online]. Available:

https://en.wikipedia.org/wiki/LTE_%28telecommunication%29. [Accessed:

24-Dec-2013].

[74] “LTE Advanced: Evolution of LTE,” LTE World. [Online]. Available:

http://lteworld.org/blog/lte-advanced-evolution-lte. [Accessed: 26-May-2015].

[75] A. A. Salah, “LTE-based passive radar for ground targets detection and

tracking,” Universiti Putra Malaysia, 2015.

© COPYRIG

HT UPM

166

[76] “List of Mobile Frequencies by Country (GSM, CDMA, UMTS, LTE).” [Online]. Available:

http://www.spectrummonitoring.com/frequencies/frequencies3.html#Malaysia

. [Accessed: 26-Jan-2016].

[77] Q. Wang, Y. L. Lu, and C. P. Hou, “Evaluation of WiMAX transmissions for passive radar applications,” Microw. Opt. Technol. Lett., vol. 52, no. 7, pp.

1507–1509, Jul. 2010.

[78] M. Rumney, LTE and the evolution to 4G wireless: design and measurement challenges, Second. John Wiley & Sons, 2013.

[79] 3GPP, “TS 36.101-910 (2009, Oct.),” 2009.

[80] “The State of LTE February 2016 - OpenSignal.” [Online]. Available: http://opensignal.com/reports/2016/02/state-of-lte-q4-2015/. [Accessed: 26-

Mar-2016].

[81] Malaysian Communications and Multimedia Commission, “MCMC Annual Report 2014,” 2014. [Online]. Available: http://www.skmm.gov.my/skmmgovmy/media/General/pdf/MCMC_AR_201

4_ENG_web.pdf.

[82] Maxis, “Our Network.” [Online]. Available: https://www.maxis.com.my/content/maxis/en/personal/internet/fastest-and-

widest-data-network.html. [Accessed: 30-Mar-2016].

[83] M. W. Live, “Malaysia’s LTE speeds near global average; Maxis leads on coverage.” [Online]. Available: http://www.mobileworldlive.com/asia/asia-

news/malaysias-lte-speeds-near-global-average-maxis-leads-on-coverage/.

[Accessed: 31-Mar-2016].

[84] “4G LTE Coverage | Digi - Let’s Inspire,” 2016. [Online]. Available: http://new.digi.com.my/services/4g-lte-coverage. [Accessed: 30-Apr-2016].

[85] T. S. Rappaport, Wireless communication principles and practice. New

Jersey: Prentice Hall, 1996.

[86] S. Sesia, M. Baker, and I. Toufik, LTE - The UMTS long term evolution: from theory to practice. John Wiley & Sons, 2011.

[87] G. J. Foschini, “Layered space-time architecture for wireless communication

in a fading environment when using multi-element antennas,” Bell Labs Tech. J., vol. 1, pp. 41–59, 1996.

[88] E. Telatar, “Capacity of multi-antenna Gaussian channels,” Eur. Trans. Telecommun., vol. 10, pp. 585–595, 1999.

© COPYRIG

HT UPM

167

[89] Y. K. Yoon, J. H. Kim, M. W. Jung, and Y. J. Chong, “Radio propagation characteristics in the large city and LTE protection from STL interference,” in 2015 17th International Conference on Advanced Communication Technology (ICACT), 2015, pp. 542–549.

[90] R. Duda, P. Hart, and D. Stork, Pattern classification (Pt.1). New York:

Wiley-Interscience, 2000.

[91] I. T. Jolliffe, Principal component analysis, 2nd ed. New York: Springer,

2002.

[92] M. Ekinci, “Human identification using gait,” Turk J Elec Engin, vol. 14, no.

2, pp. 267–291, 2006.

[93] L. Xu, L. Jie, S. Yuanming, and O. Junhuan, “SAR image denoising via clustering-based principal component analysis,” Geosci. Remote Sensing, IEEE Trans., vol. 52, no. 11, pp. 6858–6869, 2014.

[94] J. O. Kim and C. W. Mueller, Introduction to factor analysis: What it is and how to do it. Newbury Park: Sage University Paper, 1984.

[95] L. I. Smith, “A tutorial on principal component analysis,” 2002. [Online]. Available:

http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pd

f. [Accessed: 01-May-2015].

[96] N. E. Abdul Rashid, “Automatic vehicle classification in a low frequency forward scatter micro-radar,” University of Birmingham, 2011.

[97] Kishan Mehrotra, Chilakuri K. Mohan, and Sanjay Ranka, Elements of artificial neural networks. MIT Press, 1997.

[98] P. Chaudhuri, A. K. Ghosh, and H. Oja, “Classification based on hybridization of parametric and nonparametric classifiers,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 7, pp. 1153–1164, Jul. 2009.

[99] N. S. Altman, “An introduction to Kernel and Nearest-Neighbor

nonparametric regression,” Am. Stat., vol. 46, no. 3, pp. 175–185, Aug. 1992.

[100] B. G. Batchelor, “Classification and data analysis in vector spaces,” in Pattern Recognition, Springer US, 1978, pp. 65–116.

[101] R. S. A. Raja Abdullah, “Forward scattering radar for vehicle classification,” University of Birmingham, 2005.

[102] “K-Nearest Neighbors algorithm,” Wikimedia Foundation. [Online].

Available: https://en.wikipedia.org/wiki/K-

nearest_neighbors_algorithm#cite_ref-1. [Accessed: 11-Dec-2014].

© COPYRIG

HT UPM

168

[103] E. D. Solomentsev, “Euclidean space,” Encyclopedia of Mathematics.

Springer, 2011.

[104] U. S. S. and E. Commission, “AutoDesk Inc 2014 Annual Report Form (10-

K),” 2014.

[105] D. M. Pozar, Microwave Engineering, 4th ed. John Wiley & Sons Ltd, 1998.

[106] J. D. Kraus and D. A. Fleisch, Electromagnetics: With Applications, 5th ed.

McGraw-Hill Book Company, 1999.

[107] A. Goldsmith, Wireless communications. Stanford University, 2004.

[108] T.-I. Kvaksrud, “Range measurements in an open field environment,” Design Note DN018. Texas Instrum. Incorporated, Dallas, TX, p. 15, 2008.

[109] D. K. Barton and S. A. Leonov, “Radar Technology Encyclopedia.” Artech House Boston.London, 1997.

[110] M. E. Goodge, Analog electronics: analysis and design. Macmillan

Education, 1990.

[111] Joseph J. Carr, Linear IC Applications: A Designer’s Handbook. Technology

& Engineering, 1996.

[112] I. Advanced Technical Materials, “250-441EM-NF.” [Online]. Available: www.atmmicrowave.com. [Accessed: 12-May-2014].

[113] Keysight Technologies, “423B, 8470B, 8472B, 8473B/C Low barrier schottky diode detectors.” [Online]. Available: www.keysight.com.

[114] Mini-Circuits, “Low Pass Filter 50 ohm DC to 3300 MHz.” [Online]. Available: www.minicircuits.com. [Accessed: 10-Jan-2015].

[115] Measurement Computing, “USB-1608FS Series 16-Bit, Simultaneous

Sampling, Multifunction DAQ.” [Online]. Available: mccdaq.com. [Accessed: 01-Feb-2015].

[116] Garmin Ltd, “eTrex owner’s manual for use with models 10, 20, 30,” 2011. [Online]. Available: www.garmin.com. [Accessed: 01-Feb-2015].

[117] National Oceanic and Atmospheric Administration, “Beaufort wind chart –Estimating winds speeds.” [Online]. Available: www.crh.noaa.gov/Image/iwx/publications/Beaufort_Wind_Chart.pdf.

[Accessed: 01-Mar-2015].

[118] U.S. Environmental Protection Agency, “Meteorological monitoring guidance for regulatory modeling applications,” 2000.

[119] J. Taylor, “WSJT 4.6 user’s guide,” 2004.

© COPYRIG

HT UPM

169

[120] A. Reutov and V. Chapursky, “Resolution of inverted radio holograms,” Radiotechnica, vol. 6, pp. 78–83, 1987.

[121] P. M. Djuric and S. M. Kay, “Spectrum Estimation and Modeling,” in Digital Signal Processing Handbook, Boca Raton: CRC Press LLC, 1999, pp. 1–22.

[122] Piotr Ptak, “Aircraft Tracking And Classification with VHF Passive Bistatic Radar,” Lappeenranta University, 2015.

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