Advances in statistical multisource-multitarget information fusion - Mahler, Ronald P. S. (Artech House, 2014)
Includes bibliographical references and index
Preface; Acknowledgments; Chapter 1 Introduction to the Book; 1.1 OVERVIEW OF FINITE-SET STATISTICS; 1.2 RECENT ADVANCES IN FINITE-SET STATISTICS; 1.3 ORGANIZATION OF THE BOOK; Part I Elements of Finite-Set Statistics; Chapter 2 Random Finite Sets; 2.1 INTRODUCTION; 2.2 SINGLE-SENSOR, SINGLE-TARGET STATISTICS; 2.3 RANDOM FINITE SETS (RFSs); 2.4 MULTIOBJECT STATISTICS IN A NUTSHELL; Chapter 3 Multiobject Calculus; 3.1 INTRODUCTION; 3.2 BASIC CONCEPTS; 3.3 SET INTEGRALS; 3.4 MULTIOBJECT DIFFERENTIAL CALCULUS; 3.5 KEY FORMULAS OF MULTIOBJECT CALCULUS
Chapter 4 Multiobject Statistics4.1 INTRODUCTION; 4.2 BASIC MULTIOBJECT STATISTICAL DESCRIPTORS; 4.3 IMPORTANT MULTIOBJECT PROCESSES; 4.4 BASIC DERIVED RFSs; Chapter 5 Multiobject Modeling and Filtering; 5.1 INTRODUCTION; 5.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 5.3 MULTITARGET BAYES OPTIMALITY; 5.4 RFS MULTITARGET MOTION MODELS; 5.5 RFS MULTITARGET MEASUREMENT MODELS; 5.6 MULTITARGET MARKOV DENSITIES; 5.7 MULTISENSOR-MULTITARGET LIKELIHOOD FUNCTIONS; 5.8 THE MULTITARGET BAYES FILTER IN p.g.fl. FORM; 5.9 THE FACTORED MULTITARGET BAYES FILTER; 5.10 APPROXIMATE MULTITARGET FILTERS
Chapter 6 Multiobject Metrology6.1 INTRODUCTION; 6.2 MULTIOBJECT MISS DISTANCE; 6.3 MULTIOBJECT INFORMATION FUNCTIONALS; Part II RFS Filters: StandardMeasurement Model; Chapter 7 Introduction to Part II; 7.1 SUMMARY OF MAJOR LESSONS LEARNED; 7.2 STANDARD MULTITARGET MEASUREMENT MODEL; 7.3 AN APPROXIMATE STANDARD LIKELIHOOD FUNCTION; 7.4 STANDARD MULTITARGET MOTION MODEL; 7.5 STANDARD MOTION MODEL WITH TARGET SPAWNING; 7.6 ORGANIZATION OF PART II; Chapter 8 Classical PHD and CPHD Filters; 8.1 INTRODUCTION; 8.2 A GENERAL PHD FILTER; 8.3 ARBITRARY-CLUTTER PHD FILTER; 8.4 CLASSICAL PHD FILTER
8.5 CLASSICAL CARDINALIZED PHD (CPHD) FILTER8.6 ZERO FALSE ALARMS (ZFA) CPHD FILTER; 8.7 PHD FILTER FOR STATE-DEPENDENT POISSON CLUTTER; Chapter 9 Implementing Classical PHD/CPHDFilters; 9.1 INTRODUCTION; 9.2 "SPOOKY ACTION AT A DISTANCE"; 9.3 MERGING AND SPLITTING FOR PHD FILTERS; 9.4 MERGING AND SPLITTING FOR CPHD FILTERS; 9.5 GAUSSIAN MIXTURE (GM) IMPLEMENTATION; 9.6 SEQUENTIAL MONTE CARLO (SMC) IMPLEMENTATION; Chapter 10 Multisensor PHD and CPHD Filters; 10.1 INTRODUCTION; 10.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 10.3 THE GENERAL MULTISENSOR PHD FILTER