Contents. I Theoretical Concepts 1. Preface. Acknowledgments

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1 Preface Acknowledgments xi xiii I Theoretical Concepts 1 Chapter 1 Introduction Nonlinear Filtering The Problem and Its Conccptual Solution Optimal Algorithms The Kaiman Filter Grid-Based Methods Benes and Daum Filters Multiple Switching Dynamic Models Basics of Target Tracking Summary 16 Rcferences 16 Chapter 2 Suboptimal Nonlinear Filters Analytic Approximations Numerical Methods Gaussian Sum Filters Static MM Estimator Dynamic MM Filter Unscented Kaiman Filter Filtering Equations The Unscented Transform 30

2 VI 2.5 Summary Chapter 3 A Tutorial on Partie 1c Filters 3.1 Monte Carlo Integration 3.2 Sequential Importance Sampling 3.3 Resampling 3.4 Selection of Importance Density The Optimal Choiee Suboptimal Choices 3.5 Versions ofparticle Filters SIR Filter Auxiliary SIR Filter Particle Filters with an Improved Sample Diversity Local Linearization Particle Filters Multiple-Model Particle Filter 3.6 Computational Aspects 3.7 Summary 3.8 Appendix: Combination of Quadratic Terms 61 61,, oz Chapter 4 Cramer-Rao Bounds for Nonlinear Filtering 4.1 Background Recursive Computation of the Filtering Information Matrix Special Cases Additive Gaussian Noise Linear/Gaussian Gase Zero Process Noise Multiple-Switching Dynamic Models Enumeration Method Detcrministic Trajectory Summary and Further Reading II Tracking Applications Chapter 5 Tracking a Ballistic Object on Rcentry 5.1 Introduction 5.2 Target Dynamics and Measurcmcnts 5.3 Cramer-Rao Bound

3 5.4 Tracking Filters 5.5 Numerical Results 5.6 Concluding Remarks Chapterö Bearings-Only Tracking 6.1 Introduction 6.2 Problem Formulation Nonmaneuvering Case Maneuvering Case Multiple Sensor Case Tracking with Constraints 6.3 Cramer-Rao Lower Bounds Nonmaneuvering Case Maneuvering Case Multiple Sensor Case 6.4 Tracking Algorithms Nonmaneuvering Case Maneuvering Target Case Multiple Sensor Case Tracking with Hard Constraints 6.5 Simulation Results Nonmaneuvering Case Maneuvering Case Multiple Sensor Case Tracking with Hard Constraints 6.6 Summary 6.7 Appendix: Linearized Transition Matrix 1' Chapter 7 Range-Only Tracking 7.1 Introduction 7.2 Problem Description 7.3 Cramer-Rao Bounds Derivations Analysis 7.4 Tracking Algorithms 7.5 Algorithm Performance and Comparison 7.6 Application to Ingara ISAR Data 7.7 Summary

4 Vlll Chapter 8 Bistatic Radar Tracking Introduction Problem Formulation Cramer-Rao Bounds Derivations Analysis Tracking Algorithms Stage 1 of Tracker Stage 2 of Tracker Algorithm Performance Summary 199 Referenccs 201 Chapter 9 Tracking Targets Through the Blind Doppler Introduction Problem Formulation EKF-Bascd Track Maintcnance Particle Fillcr-Bascd Solution Simulation Results Summary 213 Referenccs 214 Chapter 10 Terrain-Aided Tracking Introduction Problem Dcscription and Formulation Problem Dcscription Dynamics and Measurement Models l'or VS-IMM Dynamic Models l'or VS-MMPF Variable Structure IMM Model Set Update Variable Structure Multiplc-Model Particle Filter Prediction Step Update Step Simulation Results Conclusions 236 Referenccs 237 Chapter 11 Dctection and Tracking of Slcalthy Targets Introduction Target and Sensor Models Target Model 240

5 ix Sensor Model Conceptual Solution in the Bayesian Framework A Particle Filter l'or Track-Before-Detect A Numerical Example Performance Analysis Tracking Error Performance Dctcction Performance Summary and Extcnsions Chapter 12 Group and Extended Objeet Tracking Introduction Tracking Model Formal Bayesian Solution Affine Model Particle Filters SIR Particle Filter Rao-Blackwellized Particle Filter Simulation Example Concluding Remarks Epilogue 287 Appendix Coordinate Transformations for Tracking 289 A. 1 Geodetic to ECEF and Vice Versa 290 A.2 ECEF to Tangential Plane and Vice Versa List of Acronyms 293 About the Authors 295 Index 297