Knowledge Based Radar Detection, Tracking and Classification.rar

Knowledge Based Radar Detection, Tracking and Classification (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) (Hardcover)by Fulvio Gini (Author), Muralidhar Rangaswamy (Author)


Hardcover: 268 pages
Publisher: Wiley-Interscience (May 19, 2008)
Language: English
ISBN-10: 0470149302
ISBN-13: 978-0470149300
Product Dimensions: 9.4 x 6 x 0.7 inches

Product Description
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.

From the Back Cover
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.

[ 本帖最后由 drjiachen 于 2008-12-15 11:05 编辑 ]

CONTENTS
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
v
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
vi CONTENTS
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
CONTENTS vii
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
viii CONTENTS
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
CONTENTS ix
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

期待分享，繁荣微网

Knowledge Based Radar Detection, Tracking and Classification

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[ 本帖最后由 drjiachen 于 2008-12-15 15:51 编辑 ]

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