KNOWLEDGE-BASED RADAR DETECTION, TRACKING, AND CLASSIFICATION EDITED BY Fulvio Gini and Muralidhar Rangaswamy KNOWLEDGE-BASED RADAR DETECTION, TRACKING, AND CLASSIFICATION KNOWLEDGE-BASED RADAR DETECTION, TRACKING, AND CLASSIFICATION EDITED BY Fulvio Gini and Muralidhar Rangaswamy Copyright#2008byJohnWiley&Sons,Inc.Allrightsreserved PublishedbyJohnWiley&Sons,Inc. 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Wileyalsopublishesitsbooksinvarietyofelectronicformats.Somecontentthatappearsinprintmay notbeavailableinelectronicformats.FormoreinformationaboutWileyproducts,visitourweb siteatwww.wiley.com. LibraryofCongressCataloging-in-PublicationData: Gini,Fulvio. Knowledgebasedradardetection,tracking,andclassification/FulvioGini,Muralidhar Rangaswamy p.cm. “AWiley-Intersciencepublication.” ISBN978-0-470-14930-0 1. Trackingradar. 2. Expertsystems(Computerscience). 3. Automatictracking. 4. Targetacquisition. 5. Adaptivesignalprocessing. I.Rangaswamy,Muralidhar. II. Title. TK6592.A9G562008 621.389028—dc22 2007043610 PrintedintheUnitedStatesofAmerica 10 9 8 7 6 5 4 3 2 1 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 Studyof 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 vi CONTENTS 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 Taxonomyof 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 Viewand 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 Futurework / 49 3.7.1 Target Matched Illumination / 49 3.7.2 Spectral Interpolation / 49 3.7.3 Bistatic Radarand 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 Diversityand 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 CONTENTS vii 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 Obstaclesto 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