Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR)

Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR)

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Overview

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition.

Topics covered include the problem as applied to the ground, air and maritime domains; the impact of image quality on the overall target recognition performance; the performance of different approaches to the classifier algorithm; the improvement in performance to be gained when a target can be viewed from more than one perspective; the impact of compressive sensing; advances in change detection; and challenges and directions for future research.

Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) explores both the fundamentals of classification techniques applied to data from a variety of radar modes and selected advanced techniques at the forefront of research, and is essential reading for academic, industrial and military radar researchers, students and engineers worldwide.

Product Details

ISBN-13: 9781849196857
Publisher: Institution of Engineering and Technology (IET)
Publication date: 11/28/2013
Series: Radar, Sonar and Navigation Series
Pages: 296
Product dimensions: 6.40(w) x 9.40(h) x 0.90(d)

About the Author

David Blacknell is currently a Dstl Fellow and a visiting Professor at University College London. He has worked on a large variety of topics in radar signal and image processing during his career which has included senior technical roles at DERA, QinetiQ, and Cranfield University. He is a recognised international expert on radar image exploitation and has been the recipient of two CSA commendations for contributions to Defence Science and Technology.


Hugh Griffiths holds the THALES/Royal Academy Chair of RF Sensors in the Department of Electronic and Electrical Engineering at University College London, UK, and serves as President of the IEEE AES Society for 2012/13, as Editor-in-Chief of the IET Radar, Sonar & Navigation journal, and as a member of the Defence Scientific Advisory Council for the UK Ministry of Defence. In 2012 he was awarded the A F Harvey Engineering Research Prize by the IET.

Table of Contents

1 Introduction 1

1.1 Motivation 1

1.2 Definitions and acronyms 2

1.3 Scope of book 3

2 Automatic target recognition of ground targets 5

2.1 Introduction 5

2.2 SAR phenomenology 7

2.3 The ATR processing chain 11

2.3.1 Pre-screening 11

2.3.2 Template-matching 14

2.3.3 Feature-based classification 15

2.4 Use of contextual information in target detection 19

2.4.1 Motivation 19

2.4.2 Statistical formulation 19

2.4.3 Simulated results 21

2.5 Databases and modelling 22

2.5.1 Database construction 22

2.5.2 Case study: model-based ATR using MOCEM 24

2.6 Performance assessment 26

2.6.1 Receiver operating characteristic (ROC) curves 26

2.6.2 Confusion matrices 29

2.6.3 Operational assessment 32

2.7 Conclusions 34

Acknowledgements 34

References 35

3 Automatic recognition of air targets 37

3.1 Introduction 37

3.2 Fundamentals of the target recognition process 38

3.2.1 Introduction 38

3.2.2 Target features 38

3.2.3 Aircraft recognition techniques and waveform design 39

3.2.4 Target signature measurement 41

3.2.5 Radar range equation for radar target recognition 42

3.2.6 Main classification functions 43

3.2.7 Database 44

3.2.8 Classifier 44

3.2.9 Assembly of database 46

3.2.10 Classifier performance 47

3.2.11 Conclusions 48

3.3 Jet engine recognition 48

3.3.1 Introduction 48

3.3.2 Jet engine mechanics 49

3.3.3 Interaction of radar signal with engine blades 49

3.3.4 Jet engine modulation spectrum: engine rotational rate 50

3.3.5 Jet engine modulation spectrum: rotor stage spectrum 52

3.3.6 Jet engine modulation spectrum: mixing products from rotor stages 54

3.3.7 Determination of blade count 55

3.3.8 JEM waveform 55

3.3.9 System requirements 56

3.3.10 Conclusions 56

3.4 Helicopter recognition 57

3.4.1 Introduction 57

3.4.2 Main rotor blade flash 57

3.4.3 Detection of blade flash 60

3.4.4 Waveform and system requirements for blade flash detection 62

3.4.5 Blade flash detection 62

3.4.6 Helicopter classification using blade flash 63

3.4.7 Main rotor hub spectrum 63

3.4.8 Rear rotor blades 65

3.4.9 Radar range equation for helicopter recognition 66

3.4.10 Helicopter recognition summary 67

3.5 Range-Doppler imaging 67

3.5.1 Introduction 67

3.5.2 Helicopter signature 69

3.5.3 Jet airliner signature 70

3.5.4 Business jet signature 71

3.5.5 Propeller aircraft signature 72

3.5.6 Waveforms and system requirements for supporting RDI 72

3.5.7 Conclusions 73

3.6 Aircraft target recognition conclusions 73

Acknowledgements 74

References 74

4 Radar ATR of maritime targets 77

4.1 Introduction 77

4.2 The use of high range resolution (HRR) profiles for ATR 78

4.3 The derivation of ATR features from HRR profiles 80

4.3.1 Length estimate 80

4.3.2 Position specific matrices (PSMs) 83

4.3.2.1 Determination of length 83

4.3.2.2 Alignment 83

4.3.2.3 Quantisation 83

4.3.2.4 Creation of reference PSMs 84

4.3.2.5 Compare the quantised test profile to the reference PSMs 84

4.3.2.6 Determine a figure of merit 84

4.3.2.7 Classification 86

4.3.3 Other examples of ATR features 86

4.3.4 Choosing sets of uncorrelated features 87

4.4 Ship ATR under the influence of multipath 88

4.4.1 What is multipath? 88

4.4.2 The problem of defining testing and training vectors 90

4.5 Results 92

4.5.1 Length estimate 92

4.5.1.1 Results for La and Lb based on measurements of ship HRR profiles 92

4.5.1.2 Simulation of ship HRR profiles 94

4.5.2 PSM results 96

4.5.3 Results based on geometrical, statistical and structural features 99

4.5.3.1 Measurements 99

4.5.3.2 Classification based on simulated ships 104

4.6 The mitigation of multipath effects on ship ATR 107

4.6.1 Using several antennas 109

4.6.2 Using several frequencies 110

4.6.3 Combining two antennas and two frequencies 114

4.6.4 Classification improvement via multi-frequency and/or multi-antenna approach 120

4.7 Summary 123

References 125

5 Effects of image quality on target recognition 127

5.1 Introduction 127

5.2 Improving ATR performance via PGA image quality enhancement 128

5.3 Improving ATR performance using high resolution, PWF-processed full-polarisation SAR data 131

5.4 Improving ATR performance via high-definition image processing 138

5.5 Reconstruction of interrupted SAR imagery 147

5.6 Summary and conclusions 153

References 153

6 Comparing classifier effectiveness 157

6.1 Introduction 157

6.2 NCTI studies 158

6.3 Measurements 158

6.3.1 TIRA system 158

6.3.2 Targets 160

6.4 Idea of classification 160

6.4.1 Appropriate features 160

6.4.2 HRR and 2D ISAR 161

6.4.3 2D ISAR template correlation classifier 164

6.4.4 Selection of radar parameters 166

6.5 Classification scheme 166

6.5.1 Pre-processing unit 167

6.5.2 Feature extraction/reduction 168

6.5.3 Choosing a classifier 169

6.5.4 Test of classifiers 170

6.6 Feature extraction 171

6.6.1 Classification results using different feature sets 172

6.7 Conclusion 174

References 174

7 Biologically inspired and multi-perspective target recognition 177

7.1 Introduction 177

7.2 Biologically inspired NCTR 179

7.2.1 Waveform design 179

7.2.2 Nectar-feeding bats and bat-pollinated plants 180

7.2.3 Classification of flowers 181

7.2.3.1 Data collection 182

7.2.3.2 Data pre-processing and results 182

7.2.4 Classification of insects 186

7.3 Acoustic micro-Doppler 188

7.3.1 Description of the acoustic radar 190

7.3.2 Experimentation 190

7.3.3 Classification performance results 193

7.4 Multi-aspect NCTR 194

7.4.1 Data preparation 199

7.4.2 Feature extraction 199

7.4.3 Multi-perspective classifiers 200

7.4.4 Multi-perspective classification performance 202

7.5 Summary 206

References 208

8 Radar applications of compressive sensing 213

8.1 Introduction 213

8.2 Principles of compressive sensing 214

8.2.1 Sparse and compressible signals 214

8.2.2 Restricted isometric property and coherence 216

8.2.3 Signal reconstruction 217

8.2.3.1 Minimum l2 norm reconstruction 218

8.2.3.2 Minimum l0 norm reconstruction 218

8.2.3.3 Minimum l1 norm reconstruction 218

8.2.3.4 Example of l1 norm versus l2 norm reconstruction 219

8.3 Reconstruction algorithms 220

8.3.1 Convex optimisation 220

83.1.1 Basis pursuit 220

8.3.1.2 Basis pursuit de-noising 221

8.3.1.3 Least absolute shrinkage and selection operator 221

8.3.2 Greedy constructive algorithms 222

8.3.2.1 Matching pursuit 222

8.3.2.2 Orthogonal matching pursuit 223

8.3.2.3 Stage-wise orthogonal matching pursuit 223

8.3.3 Iterative thresholding algorithms 224

8.3.3.1 Iterative hard thresholding 225

8.3.3.2 Iterative shrinkage and thresholding 226

8.4 Jet engine modulation 226

8.4.1 Introduction 226

8.4.2 Jet engine model 227

8.4.3 Simulation results of JEM compressive sensing 228

8.5 Inverse synthetic aperture radar 230

8.5.1 Introduction 230

8.5.2 Simulation model 231

8.6 Conclusions 234

Acknowledgements 234

References 234

9 Advances in SAR change detection 237

9.1 Introduction 237

9.2 An analysis of the CCD algorithm 239

9.3 Results using the 'universal image quality index' 242

9.4 Performance comparison of change detection algorithms 245

9.4.1 Visual comparisons of the MLE and CCD algorithms 253

9.4.2 Coherent change detection performance with shadow regions masked 258

9.5 Summary and conclusions 263

References 263

10 Future challenges 265

10.1 Introduction 265

10.2 Future challenges 266

10.2.1 Target variability and practical databases 266

10.2.2 Complex clutter environments 267

10.2.3 Use of contextual information 268

10.2.4 Performance assessment and prediction 269

10.2.5 Deception and countermeasures 271

References 271

Index 273

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