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Advances in Statistical Multisource-Multitarget Information Fusion.

By: Material type: TextTextPublisher: Norwood : Artech House, 2014Copyright date: ©2014Edition: 1st edDescription: 1 online resource (1167 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781608077991
Subject(s): Genre/Form: Additional physical formats: Print version:: Advances in Statistical Multisource-Multitarget Information FusionDDC classification:
  • 006.33
LOC classification:
  • QA76.76.E95 .M345 2014
Online resources:
Contents:
Intro -- Contents -- 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 Statistics -- 4.1 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 Metrology -- 6.1 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) FILTER -- 8.6 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 -- 10.4 THE MULTISENSOR CLASSICAL PHD FILTER -- 10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS -- 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS -- 10.7 AN ERRONEOUS "AVERAGED" MULTISENSOR PHD FILTER -- 10.8 PERFORMANCE COMPARISONS -- Chapter 11 Jump-Markov PHD/CPHD Filters -- 11.1 INTRODUCTION -- 11.2 JUMP-MARKOV FILTERS: A REVIEW -- 11.3 MULTITARGET JUMP-MARKOV SYSTEMS -- 11.4 JUMP-MARKOV PHD FILTER -- 11.5 JUMP-MARKOV CPHD FILTER -- 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS -- 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS -- 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS -- Chapter 12 Joint Tracking and Sensor-Bias Estimation -- 12.1 INTRODUCTION -- 12.2 MODELING SENSOR BIASES -- 12.3 OPTIMAL JOINT TRACKING AND REGISTRATION -- 12.4 THE BURT-PHD FILTER -- 12.5 SINGLE-FILTER BURT-PHD FILTERS -- 12.6 IMPLEMENTED BURT-PHD FILTERS -- Chapter 13 Multi-Bernoulli Filters -- 13.1 INTRODUCTION -- 13.2 THE BERNOULLI FILTER -- 13.3 THE MULTISENSOR BERNOULLI FILTER -- 13.4 THE CBMEMBER FILTER -- 13.5 JUMP-MARKOV CBMEMBER FILTER -- Chapter 14 RFS Multitarget Smoothers -- 14.1 INTRODUCTION -- 14.2 SINGLE-TARGET FORWARD-BACKWARD SMOOTHER -- 14.3 GENERAL MULTITARGET FORWARD-BACKWARD SMOOTHER -- 14.4 BERNOULLI FORWARD-BACKWARD SMOOTHER.
14.5 PHD FORWARD-BACKWARD SMOOTHER -- 14.6 ZTA-CPHD SMOOTHER -- Chapter 15 Exact Closed-Form Multitarget Filter -- 15.1 INTRODUCTION -- 15.2 LABELED RFSS -- 15.3 EXAMPLES OF LABELED RFSS -- 15.4 MODELING FOR THE VO-VO FILTER -- 15.5 CLOSURE OF MULTITARGET BAYES FILTER -- 15.6 IMPLEMENTATION OF THE VO-VO FILTER: SKETCH -- 15.7 PERFORMANCE RESULTS -- Part III RFS Filters for UnknownBackgrounds -- Chapter 16 Introduction to Part III -- 16.1 INTRODUCTION -- 16.2 OVERVIEW OF THE APPROACH -- 16.3 MODELS FOR UNKNOWN BACKGROUNDS -- 16.4 ORGANIZATION OF PART III -- Chapter 17 RFS Filters for Unknown pD -- 17.1 INTRODUCTION -- 17.2 THE PD-CPHD FILTER -- 17.3 BETA-GAUSSIAN MIXTURE (BGM) APPROXIMATION -- 17.4 BGM IMPLEMENTATION OF THE PD-PHD FILTER -- 17.5 BGM IMPLEMENTATION OF THE PD-CPHD FILTER -- 17.6 THE PD-CBMEMBER FILTER -- 17.7 IMPLEMENTATIONS OF PD-AGNOSTIC RFS FILTERS -- Chapter 18 RFS Filters for Unknown Clutter -- 18.1 INTRODUCTION -- 18.2 A GENERAL MODEL FOR UNKNOWN BERNOULLI CLUTTER -- 18.3 CPHD FILTER FOR GENERAL BERNOULLI CLUTTER -- 18.4 THE λ-CPHD FILTER -- 18.5 THE κ-CPHD FILTER -- 18.6 MULTISENSOR κ-CPHD FILTERS -- 18.7 THE κ-CBMEMBER FILTER -- 18.8 IMPLEMENTED CLUTTER-AGNOSTIC RFS FILTERS -- 18.9 CLUTTER-AGNOSTIC PSEUDOFILTERS -- 18.10 CPHD/PHD FILTERS WITH POISSON-MIXTURE CLUTTER -- 18.11 RELATED WORK -- Part IV RFS Filters for Nonstandard Measurement Models -- Chapter 19 RFS Filters for Superpositional Sensors -- 19.1 INTRODUCTION -- 19.2 EXACT SUPERPOSITIONAL CPHD FILTER -- 19.3 HAUSCHILDT'S APPROXIMATION -- 19.4 THOUIN-NANNURU-COATES (TNC) APPROXIMATION -- Chapter 20 RFS Filters for Pixelized Images -- 20.1 INTRODUCTION -- 20.2 THE IO MULTITARGET MEASUREMENT MODEL -- 20.3 IO MOTION MODEL -- 20.4 IO-CPHD FILTER -- 20.5 IO-MEMBER FILTER -- 20.6 IMPLEMENTATIONS OF IO-MEMBER FILTERS -- Chapter 21 RFS Filters for Cluster-Type Targets.
21.1 INTRODUCTION -- 21.2 EXTENDED-TARGET MEASUREMENT MODELS -- 21.3 EXTENDED-TARGET BERNOULLI FILTERS -- 21.4 EXTENDED-TARGET PHD/CPHD FILTERS -- 21.5 EXTENDED-TARGET CPHD FILTER: APB MODEL -- 21.6 CLUSTER-TARGET MEASUREMENT MODEL -- 21.7 CLUSTER-TARGET PHD AND CPHD FILTERS -- 21.8 MEASUREMENT MODELS FOR LEVEL-1 GROUP TARGETS -- 21.9 PHD/CPHD FILTERS FOR LEVEL-1 GROUP TARGETS -- 21.10 MEASUREMENT MODELS FOR GENERAL GROUP TARGETS -- 21.11 PHD/CPHD FILTERS FOR LEVEL-ℓ GROUP TARGETS -- 21.12 A MODEL FOR UNRESOLVED TARGETS -- 21.13 MOTION MODEL FOR UNRESOLVED TARGETS -- 21.14 THE UNRESOLVED-TARGET PHD FILTER -- 21.15 APPROXIMATE UNRESOLVED-TARGET PHD FILTER -- 21.16 APPROXIMATE UNRESOLVED-TARGET CPHD FILTER -- Chapter 22 RFS Filters for Ambiguous Measurements -- 22.1 INTRODUCTION -- 22.2 RANDOM SET MODELS OF AMBIGUOUS MEASUREMENTS -- 22.3 GENERALIZED LIKELIHOOD FUNCTIONS (GLFS) -- 22.4 UNIFICATION OF EXPERT-SYSTEM THEORIES -- 22.5 GLFS FOR IMPERFECTLY CHARACTERIZED TARGETS -- 22.6 GLFS FOR UNKNOWN TARGET TYPES -- 22.7 GLFS FOR INFORMATION WITH UNKNOWN CORRELATIONS -- 22.8 GLFS FOR UNRELIABLE INFORMATION SOURCES -- 22.9 USING GLFS IN MULTITARGET FILTERS -- 22.10 GLFS IN RFS MULTITARGET FILTERS -- 22.11 USING GLFS WITH CONVENTIONAL MULTITARGET FILTERS -- Part V Sensor, Platform, and Weapons Management -- Chapter 23 Introduction to Part V -- 23.1 BASIC ISSUES IN SENSOR MANAGEMENT -- 23.2 INFORMATION THEORY AND INTUITION: AN EXAMPLE -- 23.3 SUMMARY OF RFS SENSOR CONTROL -- 23.4 ORGANIZATION OF PART V -- Chapter 24 Single-Target Sensor Management -- 24.1 INTRODUCTION -- 24.2 EXAMPLE: MISSILE-TRACKING CAMERAS -- 24.3 SINGLE-SENSOR, SINGLE-TARGET CONTROL: MODELING -- 24.4 SINGLE-SENSOR, SINGLE-TARGET CONTROL: SINGLE-STEP -- 24.5 SINGLE-SENSOR, SINGLE-TARGET CONTROL: OBJECTIVE -- 24.6 SINGLE-SENSOR, SINGLE-TARGET CONTROL: HEDGING.
24.7 SINGLE-SENSOR, SINGLE-TARGET CONTROL: OPTIMIZATION -- 24.8 SPECIAL CASE 1: IDEAL SENSOR DYNAMICS -- 24.9 SIMPLE EXAMPLE: LINEAR-GAUSSIAN CASE -- 24.10 SPECIAL CASE 2: SIMPLIFIED NONIDEAL DYNAMICS -- Chapter 25 Multitarget Sensor Management -- 25.1 INTRODUCTION -- 25.2 MULTITARGET CONTROL: TARGET AND SENSOR STATE SPACES -- 25.3 MULTITARGET CONTROL: CONTROL SPACES -- 25.4 MULTITARGET CONTROL: MEASUREMENT SPACES -- 25.5 MULTITARGET CONTROL: MOTION MODELS -- 25.6 MULTITARGET CONTROL: MEASUREMENT MODELS -- 25.7 MULTITARGET CONTROL: SUMMARY OF NOTATION -- 25.8 MULTITARGET CONTROL: SINGLE STEP -- 25.9 MULTITARGET CONTROL: OBJECTIVE FUNCTIONS -- 25.10 MULTISENSOR-MULTITARGET CONTROL: HEDGING -- 25.11 MULTISENSOR-MULTITARGET CONTROL: OPTIMIZATION -- 25.12 SENSOR MANAGEMENT WITH IDEAL SENSOR DYNAMICS -- 25.13 SIMPLIFIED NONIDEAL MULTISENSOR DYNAMICS -- 25.14 TARGET PRIORITIZATION -- Chapter 26 Approximate Sensor Management -- 26.1 INTRODUCTION -- 26.2 SENSOR MANAGEMENT WITH BERNOULLI FILTERS -- 26.3 SENSOR MANAGEMENT WITH PHD FILTERS -- 26.4 SENSOR MANAGEMENT WITH CPHD FILTERS -- 26.5 SENSOR MANAGEMENT WITH CBMEMBER FILTERS -- 26.6 RFS SENSOR MANAGEMENT IMPLEMENTATIONS -- Appendix A Glossary of Notation and Terminology -- A.1 TRANSPARENT NOTATIONAL SYSTEM -- A.2 GENERAL MATHEMATICS -- A.3 SET THEORY -- A.4 FUZZY LOGIC AND DEMPSTER-SHAFER THEORY -- A.5 PROBABILITY AND STATISTICS -- A.6 RANDOM SETS -- A.7 MULTITARGET CALCULUS -- A.8 FINITE-SET STATISTICS -- A.9 GENERALIZED MEASUREMENTS -- Appendix B Bayesian Analysis of Dynamic Systems -- B.1 FORMAL BAYES MODELING IN GENERAL -- B.2 THE BAYES FILTER IN GENERAL -- Appendix C Rigorous Functional Derivatives -- C.1 NONCONSTRUCTIVE DEFINITION OF THE FUNCTIONAL DERIVATIVE -- C.2 THE CONSTRUCTIVE RADON-NIKOD´YM DERIVATIVE -- C.3 CONSTRUCTIVE DEFINITION OF THE FUNCTIONAL DERIVATIVE.
Appendix D Partitions of Finite Sets.
Summary: This is the sequel to the 2007 Artech House bestselling title, Statistical Multisource-Multitarget Information Fusion. That earlier book was a comprehensive resource for an in-depth understanding of finite-set statistics (FISST), a unified, systematic, and Bayesian approach to information fusion. The cardinalized probability hypothesis density (CPHD) filter, which was first systematically described in the earlier book, has since become a standard multitarget detection and tracking technique, especially in research and development.Since 2007, FISST has inspired a considerable amount of research, conducted in more than a dozen nations, and reported in nearly a thousand publications. This sequel addresses the most intriguing practical and theoretical advances in FISST, for the first time aggregating and systematizing them into a coherent, integrated, and deep-dive picture. Special emphasis is given to computationally fast exact closed-form implementation approaches. The book also includes the first complete and systematic description of RFS-based sensor/platform management and situation assessment.
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Intro -- Contents -- 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 Statistics -- 4.1 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 Metrology -- 6.1 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) FILTER -- 8.6 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 -- 10.4 THE MULTISENSOR CLASSICAL PHD FILTER -- 10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS -- 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS -- 10.7 AN ERRONEOUS "AVERAGED" MULTISENSOR PHD FILTER -- 10.8 PERFORMANCE COMPARISONS -- Chapter 11 Jump-Markov PHD/CPHD Filters -- 11.1 INTRODUCTION -- 11.2 JUMP-MARKOV FILTERS: A REVIEW -- 11.3 MULTITARGET JUMP-MARKOV SYSTEMS -- 11.4 JUMP-MARKOV PHD FILTER -- 11.5 JUMP-MARKOV CPHD FILTER -- 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS -- 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS -- 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS -- Chapter 12 Joint Tracking and Sensor-Bias Estimation -- 12.1 INTRODUCTION -- 12.2 MODELING SENSOR BIASES -- 12.3 OPTIMAL JOINT TRACKING AND REGISTRATION -- 12.4 THE BURT-PHD FILTER -- 12.5 SINGLE-FILTER BURT-PHD FILTERS -- 12.6 IMPLEMENTED BURT-PHD FILTERS -- Chapter 13 Multi-Bernoulli Filters -- 13.1 INTRODUCTION -- 13.2 THE BERNOULLI FILTER -- 13.3 THE MULTISENSOR BERNOULLI FILTER -- 13.4 THE CBMEMBER FILTER -- 13.5 JUMP-MARKOV CBMEMBER FILTER -- Chapter 14 RFS Multitarget Smoothers -- 14.1 INTRODUCTION -- 14.2 SINGLE-TARGET FORWARD-BACKWARD SMOOTHER -- 14.3 GENERAL MULTITARGET FORWARD-BACKWARD SMOOTHER -- 14.4 BERNOULLI FORWARD-BACKWARD SMOOTHER.

14.5 PHD FORWARD-BACKWARD SMOOTHER -- 14.6 ZTA-CPHD SMOOTHER -- Chapter 15 Exact Closed-Form Multitarget Filter -- 15.1 INTRODUCTION -- 15.2 LABELED RFSS -- 15.3 EXAMPLES OF LABELED RFSS -- 15.4 MODELING FOR THE VO-VO FILTER -- 15.5 CLOSURE OF MULTITARGET BAYES FILTER -- 15.6 IMPLEMENTATION OF THE VO-VO FILTER: SKETCH -- 15.7 PERFORMANCE RESULTS -- Part III RFS Filters for UnknownBackgrounds -- Chapter 16 Introduction to Part III -- 16.1 INTRODUCTION -- 16.2 OVERVIEW OF THE APPROACH -- 16.3 MODELS FOR UNKNOWN BACKGROUNDS -- 16.4 ORGANIZATION OF PART III -- Chapter 17 RFS Filters for Unknown pD -- 17.1 INTRODUCTION -- 17.2 THE PD-CPHD FILTER -- 17.3 BETA-GAUSSIAN MIXTURE (BGM) APPROXIMATION -- 17.4 BGM IMPLEMENTATION OF THE PD-PHD FILTER -- 17.5 BGM IMPLEMENTATION OF THE PD-CPHD FILTER -- 17.6 THE PD-CBMEMBER FILTER -- 17.7 IMPLEMENTATIONS OF PD-AGNOSTIC RFS FILTERS -- Chapter 18 RFS Filters for Unknown Clutter -- 18.1 INTRODUCTION -- 18.2 A GENERAL MODEL FOR UNKNOWN BERNOULLI CLUTTER -- 18.3 CPHD FILTER FOR GENERAL BERNOULLI CLUTTER -- 18.4 THE λ-CPHD FILTER -- 18.5 THE κ-CPHD FILTER -- 18.6 MULTISENSOR κ-CPHD FILTERS -- 18.7 THE κ-CBMEMBER FILTER -- 18.8 IMPLEMENTED CLUTTER-AGNOSTIC RFS FILTERS -- 18.9 CLUTTER-AGNOSTIC PSEUDOFILTERS -- 18.10 CPHD/PHD FILTERS WITH POISSON-MIXTURE CLUTTER -- 18.11 RELATED WORK -- Part IV RFS Filters for Nonstandard Measurement Models -- Chapter 19 RFS Filters for Superpositional Sensors -- 19.1 INTRODUCTION -- 19.2 EXACT SUPERPOSITIONAL CPHD FILTER -- 19.3 HAUSCHILDT'S APPROXIMATION -- 19.4 THOUIN-NANNURU-COATES (TNC) APPROXIMATION -- Chapter 20 RFS Filters for Pixelized Images -- 20.1 INTRODUCTION -- 20.2 THE IO MULTITARGET MEASUREMENT MODEL -- 20.3 IO MOTION MODEL -- 20.4 IO-CPHD FILTER -- 20.5 IO-MEMBER FILTER -- 20.6 IMPLEMENTATIONS OF IO-MEMBER FILTERS -- Chapter 21 RFS Filters for Cluster-Type Targets.

21.1 INTRODUCTION -- 21.2 EXTENDED-TARGET MEASUREMENT MODELS -- 21.3 EXTENDED-TARGET BERNOULLI FILTERS -- 21.4 EXTENDED-TARGET PHD/CPHD FILTERS -- 21.5 EXTENDED-TARGET CPHD FILTER: APB MODEL -- 21.6 CLUSTER-TARGET MEASUREMENT MODEL -- 21.7 CLUSTER-TARGET PHD AND CPHD FILTERS -- 21.8 MEASUREMENT MODELS FOR LEVEL-1 GROUP TARGETS -- 21.9 PHD/CPHD FILTERS FOR LEVEL-1 GROUP TARGETS -- 21.10 MEASUREMENT MODELS FOR GENERAL GROUP TARGETS -- 21.11 PHD/CPHD FILTERS FOR LEVEL-ℓ GROUP TARGETS -- 21.12 A MODEL FOR UNRESOLVED TARGETS -- 21.13 MOTION MODEL FOR UNRESOLVED TARGETS -- 21.14 THE UNRESOLVED-TARGET PHD FILTER -- 21.15 APPROXIMATE UNRESOLVED-TARGET PHD FILTER -- 21.16 APPROXIMATE UNRESOLVED-TARGET CPHD FILTER -- Chapter 22 RFS Filters for Ambiguous Measurements -- 22.1 INTRODUCTION -- 22.2 RANDOM SET MODELS OF AMBIGUOUS MEASUREMENTS -- 22.3 GENERALIZED LIKELIHOOD FUNCTIONS (GLFS) -- 22.4 UNIFICATION OF EXPERT-SYSTEM THEORIES -- 22.5 GLFS FOR IMPERFECTLY CHARACTERIZED TARGETS -- 22.6 GLFS FOR UNKNOWN TARGET TYPES -- 22.7 GLFS FOR INFORMATION WITH UNKNOWN CORRELATIONS -- 22.8 GLFS FOR UNRELIABLE INFORMATION SOURCES -- 22.9 USING GLFS IN MULTITARGET FILTERS -- 22.10 GLFS IN RFS MULTITARGET FILTERS -- 22.11 USING GLFS WITH CONVENTIONAL MULTITARGET FILTERS -- Part V Sensor, Platform, and Weapons Management -- Chapter 23 Introduction to Part V -- 23.1 BASIC ISSUES IN SENSOR MANAGEMENT -- 23.2 INFORMATION THEORY AND INTUITION: AN EXAMPLE -- 23.3 SUMMARY OF RFS SENSOR CONTROL -- 23.4 ORGANIZATION OF PART V -- Chapter 24 Single-Target Sensor Management -- 24.1 INTRODUCTION -- 24.2 EXAMPLE: MISSILE-TRACKING CAMERAS -- 24.3 SINGLE-SENSOR, SINGLE-TARGET CONTROL: MODELING -- 24.4 SINGLE-SENSOR, SINGLE-TARGET CONTROL: SINGLE-STEP -- 24.5 SINGLE-SENSOR, SINGLE-TARGET CONTROL: OBJECTIVE -- 24.6 SINGLE-SENSOR, SINGLE-TARGET CONTROL: HEDGING.

24.7 SINGLE-SENSOR, SINGLE-TARGET CONTROL: OPTIMIZATION -- 24.8 SPECIAL CASE 1: IDEAL SENSOR DYNAMICS -- 24.9 SIMPLE EXAMPLE: LINEAR-GAUSSIAN CASE -- 24.10 SPECIAL CASE 2: SIMPLIFIED NONIDEAL DYNAMICS -- Chapter 25 Multitarget Sensor Management -- 25.1 INTRODUCTION -- 25.2 MULTITARGET CONTROL: TARGET AND SENSOR STATE SPACES -- 25.3 MULTITARGET CONTROL: CONTROL SPACES -- 25.4 MULTITARGET CONTROL: MEASUREMENT SPACES -- 25.5 MULTITARGET CONTROL: MOTION MODELS -- 25.6 MULTITARGET CONTROL: MEASUREMENT MODELS -- 25.7 MULTITARGET CONTROL: SUMMARY OF NOTATION -- 25.8 MULTITARGET CONTROL: SINGLE STEP -- 25.9 MULTITARGET CONTROL: OBJECTIVE FUNCTIONS -- 25.10 MULTISENSOR-MULTITARGET CONTROL: HEDGING -- 25.11 MULTISENSOR-MULTITARGET CONTROL: OPTIMIZATION -- 25.12 SENSOR MANAGEMENT WITH IDEAL SENSOR DYNAMICS -- 25.13 SIMPLIFIED NONIDEAL MULTISENSOR DYNAMICS -- 25.14 TARGET PRIORITIZATION -- Chapter 26 Approximate Sensor Management -- 26.1 INTRODUCTION -- 26.2 SENSOR MANAGEMENT WITH BERNOULLI FILTERS -- 26.3 SENSOR MANAGEMENT WITH PHD FILTERS -- 26.4 SENSOR MANAGEMENT WITH CPHD FILTERS -- 26.5 SENSOR MANAGEMENT WITH CBMEMBER FILTERS -- 26.6 RFS SENSOR MANAGEMENT IMPLEMENTATIONS -- Appendix A Glossary of Notation and Terminology -- A.1 TRANSPARENT NOTATIONAL SYSTEM -- A.2 GENERAL MATHEMATICS -- A.3 SET THEORY -- A.4 FUZZY LOGIC AND DEMPSTER-SHAFER THEORY -- A.5 PROBABILITY AND STATISTICS -- A.6 RANDOM SETS -- A.7 MULTITARGET CALCULUS -- A.8 FINITE-SET STATISTICS -- A.9 GENERALIZED MEASUREMENTS -- Appendix B Bayesian Analysis of Dynamic Systems -- B.1 FORMAL BAYES MODELING IN GENERAL -- B.2 THE BAYES FILTER IN GENERAL -- Appendix C Rigorous Functional Derivatives -- C.1 NONCONSTRUCTIVE DEFINITION OF THE FUNCTIONAL DERIVATIVE -- C.2 THE CONSTRUCTIVE RADON-NIKOD´YM DERIVATIVE -- C.3 CONSTRUCTIVE DEFINITION OF THE FUNCTIONAL DERIVATIVE.

Appendix D Partitions of Finite Sets.

This is the sequel to the 2007 Artech House bestselling title, Statistical Multisource-Multitarget Information Fusion. That earlier book was a comprehensive resource for an in-depth understanding of finite-set statistics (FISST), a unified, systematic, and Bayesian approach to information fusion. The cardinalized probability hypothesis density (CPHD) filter, which was first systematically described in the earlier book, has since become a standard multitarget detection and tracking technique, especially in research and development.Since 2007, FISST has inspired a considerable amount of research, conducted in more than a dozen nations, and reported in nearly a thousand publications. This sequel addresses the most intriguing practical and theoretical advances in FISST, for the first time aggregating and systematizing them into a coherent, integrated, and deep-dive picture. Special emphasis is given to computationally fast exact closed-form implementation approaches. The book also includes the first complete and systematic description of RFS-based sensor/platform management and situation assessment.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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