Advances in Statistical Multisource-Multitarget Information Fusion

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• Publisher: Artech House
• Year: 2014
• eBook ISBN: 9781608077991
• Edition: 0
• Topic: Technology & Engineering
• Index: Technology & Engineering

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.

Table of contents

• Contents
• Preface
• Acknowledgments
• Chapter 1 Introduction to the Book
• Part I Elements of Finite-Set Statistics
• Chapter 2 Random Finite Sets
• Chapter 3 Multiobject Calculus
• Chapter 4 Multiobject Statistics
• Chapter 5 Multiobject Modeling and Filtering
• Chapter 6 Multiobject Metrology
• Part II RFS Filters: StandardMeasurement Model
• Chapter 7 Introduction to Part II
• Chapter 8 Classical PHD and CPHD Filters
• Chapter 9 Implementing Classical PHD/CPHDFilters
• Chapter 10 Multisensor PHD and CPHD Filters
• Chapter 11 Jump-Markov PHD/CPHD Filters
• Chapter 12 Joint Tracking and Sensor-Bias Estimation
• Chapter 13 Multi-Bernoulli Filters
• Chapter 14 RFS Multitarget Smoothers
• Chapter 15 Exact Closed-Form Multitarget Filter
• Part III RFS Filters for UnknownBackgrounds
• Chapter 16 Introduction to Part III
• Chapter 17 RFS Filters for Unknown pD
• Chapter 18 RFS Filters for Unknown Clutter
• Part IV RFS Filters for Nonstandard Measurement Models
• Chapter 19 RFS Filters for Superpositional Sensors
• Chapter 20 RFS Filters for Pixelized Images
• Chapter 21 RFS Filters for Cluster-Type Targets
• Chapter 22 RFS Filters for Ambiguous Measurements
• Part V Sensor, Platform, and Weapons Management
• Chapter 23 Introduction to Part V
• Chapter 24 Single-Target Sensor Management
• Chapter 25 Multitarget Sensor Management
• Chapter 26 Approximate Sensor Management
• Appendix A Glossary of Notation and Terminology
• Appendix B Bayesian Analysis of Dynamic Systems
• Appendix C Rigorous Functional Derivatives
• Appendix D Partitions of Finite Sets
• Appendix E Beta Distributions
• Appendix F Markov Time Update of Beta Distributions
• Appendix G Normal-Wishart Distributions
• Appendix H Complex-Number Gaussian Distributions
• Appendix I Statistics of Level-1 Group Targets
• Appendix J FISST Calculus and Moyal’s Calculus
• Appendix K Mathematical Derivations
• References
• About the Author
• Index