Knowledge Based Radar Detection, Tracking and Classification
, by Gini, Fulvio; Rangaswamy, Muralidhar- ISBN: 9780470149300 | 0470149302
- Cover: Hardcover
- Copyright: 5/19/2008
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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