Knowledge Based Radar Detection, Tracking and

This book brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. It highlights the latest advances in the field and forecasts the impact of KB technology on future systems, including civilian, military, and homeland defense applications. Throughout the book, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability.

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	p. xi
Introduction	p. 1
Organization of the Book	p. 3
Acknowledgments	p. 7
References	p. 7
Cognitive Radar	p. 9
Introduction	p. 9
Cognitive Radar Signal-Processing Cycle	p. 10
Radar-Scene Analysis	p. 12
Statistical Modeling of Statistical Representation of Clutter- and Target-Related Information	p. 13
Bayesian Target Tracking	p. 14
One-Step Tracking Prediction	p. 16
Tracking Filter	p. 16
Tracking Smoother	p. 18
Experimental Results: Case Study of Small Target in Sea Clutter	p. 19
Practical Implications of the Bayesian Target Tracker	p. 20
Adaptive Radar Illumination	p. 21
Simulation Experiments in Support of Adjustable Frequency Modulation	p. 22
Echo-Location in Bats	p. 23
Discussion	p. 25
Learning	p. 27
Applications	p. 27
Multifunction Radars	p. 27
Noncoherent Radar Network	p. 28
Acknowledgments	p. 29
References	p. 29
Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview	p. 31
Radar Evolution	p. 32
Taxonomy of Radar	p. 34
Signal Processing	p. 35
Data Processing	p. 37
Introduction to Artificial Intelligence	p. 38
Why Robotics and Knowledge-Based Systems?	p. 39
Knowledge Base Systems (KBS)	p. 39
Semantic Web Technologies	p. 40
A Global View and KB Algorithms	p. 40
An Airborne Autonomous Intelligent Radar System (AIRS)	p. 42
Filtering, Detection, and Tracking Algorithms and KB Processing	p. 44
Future work	p. 49
Target Matched Illumination	p. 49
Spectral Interpolation	p. 49
Bistatic Radar and Passive Coherent Location	p. 50
Synthetic Aperture Radar	p. 50
Resource Allocation in a Multifunction Phased Array Radar	p. 50
Waveform Diversity and Sensor Geometry	p. 51
Acknowledgments	p. 51
References	p. 51
An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond	p. 55
Introduction	p. 56
Background on STAP	p. 56
Examples of Real-World Clutter	p. 60
Knowledge-Aided STAP (KA-STAP)	p. 61
Knowledge-Aided STAP: Back to "Bayes-ics"	p. 61
Case I: Intelligent Training and Filter Selection (ITFS)	p. 62
Case II: Bayesian Filtering and Data Pre-Whitening	p. 63
Real-Time KA-STAP: The DARPA KASSPER Program	p. 67
Obstacles to Real-Time KA-STAP	p. 67
Solution: Look-Ahead Scheduling	p. 67
Applying KA Processing to the Adaptive MIMO Radar Problem	p. 71
The Future: Next-Generation Intelligent Adaptive Sensors	p. 72
References	p. 72
Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective	p. 75
Introduction	p. 76
Problem Statement	p. 77
Low Computation Load Algorithms	p. 81
Joint Domain Localized Processing	p. 82
Parametric Adaptive Matched Filter	p. 84
Multistage Wiener Filter	p. 85
Issues of Data Support	p. 86
Nonhomogeneity Detection	p. 87
Direct Data Domain Methods	p. 89
Hybrid Approach	p. 90
Knowledge-Aided Approaches	p. 91
A Preliminary Knowledge-Based Processor	p. 92
Numerical Example	p. 94
A Long-Term View	p. 98
Conclusions	p. 99
References	p. 99
CFAR Knowledge-Aided Radar Detection and its Demonstration Using Measured Airborne Data	p. 103
Introduction	p. 103
Problem Formulation and Design Issues	p. 106
KA Data Selector	p. 107
2S-DSP Data Selection Procedure	p. 109
Two-Step Data Selection Procedure (2S-DSP)	p. 112
RP-ANMF Detector	p. 113
Performance Analysis	p. 114
Conclusions	p. 123
References	p. 123
Appendix 6A: Registration Geometry	p. 127
STAP via Knowledge-Aided Covariance Estimation and the FRACTA Meta-Algorithm	p. 129
Introduction	p. 130
The FRACTA Meta-Algorithm	p. 132
The General STAP Model	p. 132
FRACTA Description	p. 134
Reiterative Censoring	p. 135
CFAR Detector	p. 137
ACE Detector	p. 138
Practical Aspects of Censoring	p. 139
Global Censoring	p. 139
Censoring Stopping Criterion	p. 140
Fast Reiterative Censoring	p. 141
FRACTA Performance	p. 141
Knowledge-Aided FRACTA	p. 147
Knowledge-Aided Covariance Estimation	p. 147
Doppler-Sensitive ACE Detector	p. 149
Performance of Knowledge-Aided FRACTA	p. 151
Partially Adaptive FRACTA	p. 156
Reduced-Dimension STAP	p. 157
Multiwindow Post-Doppler STAP	p. 157
PRI-Staggered Post-Doppler STAP	p. 159
Adjacent-Bin Post-Doppler STAP	p. 160
Multiwindow Post-Doppler FRACTA	p. 160
Multiwindow Post-Doppler FRACTA + KACE	p. 161
Performance of Partially Adaptive FRACTA + KACE	p. 161
Conclusions	p. 163
References	p. 163
Knowledge-Based Radar Tracking	p. 167
Introduction	p. 167
Architecture of the Tracking Filter	p. 169
Filtering	p. 169
Data Association	p. 172
Track Initiation	p. 174
Tracking with Geographical Information	p. 176
Processing of Geographical Maps	p. 178
Hard Classification	p. 179
Fuzzy Classification	p. 179
Application of the KB to the Tracking System	p. 180
Hard Classification: DMHC and DTPHC	p. 182
Fuzzy Classification: DMLR and [alpha]-NNCJPDA	p. 183
Knowledge-Based Target ID	p. 184
Tracking with Amplitude Information	p. 185
Performance Evaluation	p. 187
Aircraft Simulation Results	p. 189
Number of False Tracks and Tentative Tracks	p. 192
The Use of Amplitude Information	p. 193
Conclusions	p. 194
Acknowledgments	p. 194
References	p. 195
Knowledge-Based Radar Target Classification	p. 197
Introduction	p. 197
Database	p. 200
Target Recognition by Human Operator	p. 203
Classification Scheme	p. 203
Knowledge-Based Models	p. 205
Statistical Knowledge-Based Approach	p. 206
Physical Knowledge-Based Approach	p. 207
Physical Model Construction	p. 208
Indirect Concept	p. 213
Direct Concept	p. 214
Combined Approach	p. 215
Experimental Results	p. 215
Statistical Knowledge-Based Classifier for the Seven-Class Problem	p. 216
Physical Knowledge-Based Classifier for the Three-Class Problem	p. 218
Conclusions	p. 222
References	p. 223
Multifunction Radar Resource Management	p. 225
Introduction	p. 225
Simulation Architecture	p. 229
Priority Assignment	p. 230
Surveillance Manager	p. 230
Track Manager	p. 230
Radar Functions	p. 231
Operator and Strategy	p. 231
The Schedulers	p. 231
Orman et al. Type Scheduler	p. 231
Butler-Type Scheduler	p. 232
Comparison of the Scheduling Algorithms	p. 232
Underload Situations	p. 234
Overload Situations	p. 238
Scheduling Issues	p. 243
Prioritization of Radar Tasks	p. 244
Prioritization of Tracking Tasks	p. 245
Prioritization of Sectors of Surveillance	p. 246
Examination of the Fuzzy Logic Method	p. 248
Comparison of the Different Prioritization Methods	p. 253
Prioritization Issues	p. 261
Summary and Conclusions	p. 262
References	p. 262
Index	p. 265
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Knowledge Based Radar Detection, Tracking and Classification

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