Knowledge Based Radar Detection, Tracking and Classification
Wiley-Interscience (Verlag)
978-0-470-14930-0 (ISBN)
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Discover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems.
The book begins with a chapter introducing the concept of Knowledge Based (KB) radar.
The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are:
Fundamentals of relevant KB techniques
KB solutions as they apply to the general radar problem
KBS applications for the constant false-alarm rate processor
KB control for space-time adaptive processing
KB techniques applied to existing radar systems
Integrated end-to-end radar signals
Data processing with overarching KB control
All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications.
With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.
Fulvio Gini, PhD, IEEE Fellow, is a Full Professor at the University of Pisa, Italy. He was the technical program cochairman of the 2006 EURASIP Signal and Image Processing Conference (Florence, Italy) and the 2008 IEEE Radar Conference (Rome, Italy). His research interests include radar signal processing; cyclostationary signal analysis; non-Gaussian signal modeling, detection, and estimation; and parameter estimation and data extraction from multichannel interferometric SAR data. Professor Gini has coauthored more than eighty refereed journal papers, more than eighty conference papers, and three book chapters. Muralidhar Rangaswamy, PhD, IEEE Fellow, is the Technical Advisor for the Radar Signal Processing Branch at the Sensors Directorate of the Air Force Research Laboratory (AFRL). His research interests include radar signal processing, spectrum estimation, modeling non-Gaussian interference phenomena, and statistical communication theory. Dr. Rangaswamy has coauthored more than eighty refereed journal and conference papers. In addition, he is a contributor to three books and a coinventor on two U.S. patents.
Contributors xi
1 Introduction 1
Fulvio Gini and Muralidhar Rangaswamy
1.1 Organization of the Book 3
Acknowledgments 7
References 7
2 Cognitive Radar 9
Simon Haykin
2.1 Introduction 9
2.2 Cognitive Radar Signal-Processing Cycle 10
2.3 Radar-Scene Analysis 12
2.3.1 Statistical Modeling of Statistical Representation of Clutter- and Target-Related Information 13
2.4 Bayesian Target Tracking 14
2.4.1 One-Step Tracking Prediction 16
2.4.2 Tracking Filter 16
2.4.3 Tracking Smoother 18
2.4.4 Experimental Results: Case Study of Small Target in Sea Clutter 19
2.4.5 Practical Implications of the Bayesian Target Tracker 20
2.5 Adaptive Radar Illumination 21
2.5.1 Simulation Experiments in Support of Adjustable Frequency Modulation 22
2.6 Echo-Location in Bats 23
2.7 Discussion 25
2.7.1 Learning 27
2.7.2 Applications 27
2.7.2.1 Multifunction Radars 27
2.7.2.2 Noncoherent Radar Network 28
Acknowledgments 29
References 29
3 Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview 31
Gerard T. Capraro, Alfonso Farina, Hugh D. Griffiths, and Michael C. Wicks
3.1 Radar Evolution 32
3.2 Taxonomy of Radar 34
3.3 Signal Processing 35
3.4 Data Processing 37
3.5 Introduction to Artificial Intelligence 38
3.5.1 Why Robotics and Knowledge-Based Systems? 39
3.5.2 Knowledge Base Systems (KBS) 39
3.5.3 Semantic Web Technologies 40
3.6 A Global View and KB Algorithms 40
3.6.1 An Airborne Autonomous Intelligent Radar System (AIRS) 42
3.6.2 Filtering, Detection, and Tracking Algorithms and KB Processing 44
3.7 Future work 49
3.7.1 Target Matched Illumination 49
3.7.2 Spectral Interpolation 49
3.7.3 Bistatic Radar and Passive Coherent Location 50
3.7.4 Synthetic Aperture Radar 50
3.7.5 Resource Allocation in a Multifunction Phased Array Radar 50
3.7.6 Waveform Diversity and Sensor Geometry 51
Acknowledgments 51
References 51
4 An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond 55
Joseph R. Guerci and Edward J. Baranoski
4.1 Introduction 56
4.1.1 Background on STAP 56
4.1.2 Examples of Real-World Clutter 60
4.2 Knowledge-Aided STAP (KA-STAP) 61
4.2.1 Knowledge-Aided STAP: Back to “Bayes-ics” 61
4.2.1.1 Case I: Intelligent Training and Filter Selection (ITFS) 62
4.2.1.2 Case II: Bayesian Filtering and Data Pre-Whitening 63
4.3 Real-Time KA-STAP: The DARPA KASSPER Program 67
4.3.1 Obstacles to Real-Time KA-STAP 67
4.3.2 Solution: Look-Ahead Scheduling 67
4.4 Applying KA Processing to the Adaptive MIMO Radar Problem 71
4.5 The Future: Next-Generation Intelligent Adaptive Sensors 72
References 72
5 Space–Time Adaptive Processing for Airborne Radar: A Knowledge–Based Perspective 75
Michael C. Wicks, Muralidhar Rangaswamy, Raviraj S. Adve, and Todd B. Hale
5.1 Introduction 76
5.2 Problem Statement 77
5.3 Low Computation Load Algorithms 81
5.3.1 Joint Domain Localized Processing 82
5.3.2 Parametric Adaptive Matched Filter 84
5.3.3 Multistage Wiener Filter 85
5.4 Issues of Data Support 86
5.4.1 Nonhomogeneity Detection 87
5.4.2 Direct Data Domain Methods 89
5.4.2.1 Hybrid Approach 90
5.5 Knowledge-Aided Approaches 91
5.5.1 A Preliminary Knowledge-Based Processor 92
5.5.2 Numerical Example 94
5.5.3 A Long-Term View 98
5.6 Conclusions 99
References 99
6 CFAR Knowledge-Aided Radar Detection and its Demonstration Using Measured Airborne Data 103
Christopher T. Capraro, Gerard T. Capraro, Antonio De Maio, Alfonso Farina, and Michael C. Wicks
6.1 Introduction 103
6.2 Problem Formulation and Design Issues 106
6.3 KA Data Selector 107
6.4 2S-DSP Data Selection Procedure 109
6.4.1 Two-Step Data Selection Procedure (2S-DSP) 112
6.5 RP-ANMF Detector 113
6.6 Performance Analysis 114
6.7 Conclusions 123
References 123
Appendix 6A: Registration Geometry 127
7 STAP via Knowledge-Aided Covariance Estimation and the FRACTA Meta-Algorithm 129
Shannon D. Blunt, Karl Gerlach, Muralidhar Rangaswamy, and Aaron K. Shackelford
7.1 Introduction 130
7.2 The FRACTA Meta-Algorithm 132
7.2.1 The General STAP Model 132
7.2.2 FRACTA Description 134
7.2.2.1 Reiterative Censoring 135
7.2.2.2 CFAR Detector 137
7.2.2.3 ACE Detector 138
7.3 Practical Aspects of Censoring 139
7.3.1 Global Censoring 139
7.3.2 Censoring Stopping Criterion 140
7.3.3 Fast Reiterative Censoring 141
7.3.4 FRACTA Performance 141
7.4 Knowledge-Aided FRACTA 147
7.4.1 Knowledge-Aided Covariance Estimation 147
7.4.2 Doppler-Sensitive ACE Detector 149
7.4.3 Performance of Knowledge-Aided FRACTA 151
7.5 Partially Adaptive FRACTA 156
7.5.1 Reduced-Dimension STAP 157
7.5.2 Multiwindow Post-Doppler STAP 157
7.5.2.1 PRI-Staggered Post-Doppler STAP 159
7.5.2.2 Adjacent-Bin Post-Doppler STAP 160
7.5.3 Multiwindow Post-Doppler FRACTA 160
7.5.4 Multiwindow Post-Doppler FRACTA þ KACE 161
7.5.5 Performance of Partially Adaptive FRACTA þ KACE 161
7.6 Conclusions 163
References 163
8 Knowledge-Based Radar Tracking 167
Alessio Benavoli, Luigi Chisci, Alfonso Farina, Sandro Immediata, and Luca Timmoneri
8.1 Introduction 167
8.2 Architecture of the Tracking Filter 169
8.2.1 Filtering 169
8.2.2 Data Association 172
8.2.3 Track Initiation 174
8.3 Tracking with Geographical Information 176
8.3.1 Processing of Geographical Maps 178
8.3.2 Hard Classification 179
8.3.3 Fuzzy Classification 179
8.3.4 Application of the KB to the Tracking System 180
8.3.5 Hard Classification: DMHC and Dtphc 182
8.3.6 Fuzzy Classification: DMLR and a-NNCJPDA 183
8.4 Knowledge-Based Target ID 184
8.5 Tracking with Amplitude Information 185
8.6 Performance Evaluation 187
8.6.1 Aircraft Simulation Results 189
8.6.2 Number of False Tracks and Tentative Tracks 192
8.6.3 The Use of Amplitude Information 193
8.7 Conclusions 194
Acknowledgments 194
References 195
9 Knowledge-Based Radar Target Classification 197
Igal Bilik and Joseph Tabrikian
9.1 Introduction 197
9.2 Database 200
9.3 Target Recognition by Human Operator 203
9.4 Classification Scheme 203
9.4.1 Knowledge-Based Models 205
9.4.2 Statistical Knowledge-Based Approach 206
9.5 Physical Knowledge-Based Approach 207
9.5.1 Physical Model Construction 208
9.5.2 Indirect Concept 213
9.5.3 Direct Concept 214
9.6 Combined Approach 215
9.7 Experimental Results 215
9.7.1 Statistical Knowledge-Based Classifier for the Seven-Class Problem 216
9.7.2 Physical Knowledge-Based Classifier for the Three-Class Problem 218
9.8 Conclusions 222
References 223
10 Multifunction Radar Resource Management 225
Sergio Luis de Carvalho Miranda, Chris J. Baker, Karl Woodbridge, and Hugh D. Griffiths
10.1 Introduction 225
10.2 Simulation Architecture 229
10.2.1 Priority Assignment 230
10.2.2 Surveillance Manager 230
10.2.3 Track Manager 230
10.2.4 Radar Functions 231
10.2.5 Operator and Strategy 231
10.3 The Schedulers 231
10.3.1 Orman et al. Type Scheduler 231
10.3.2 Butler-Type Scheduler 232
10.4 Comparison of the Scheduling Algorithms 232
10.4.1 Underload Situations 234
10.4.2 Overload Situations 238
10.5 Scheduling Issues 243
10.6 Prioritization of Radar Tasks 244
10.6.1 Prioritization of Tracking Tasks 245
10.6.2 Prioritization of Sectors of Surveillance 246
10.7 Examination of the Fuzzy Logic Method 248
10.8 Comparison of the Different Prioritization Methods 253
10.9 Prioritization Issues 261
10.10 Summary and Conclusions 262
References 262
Index 265
| Erscheint lt. Verlag | 6.6.2008 |
|---|---|
| Reihe/Serie | Adaptive and Learning Systems for Signal Processing, Communications, and Control Series ; 1 |
| Sprache | englisch |
| Maße | 162 x 240 mm |
| Gewicht | 533 g |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 0-470-14930-2 / 0470149302 |
| ISBN-13 | 978-0-470-14930-0 / 9780470149300 |
| Zustand | Neuware |
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