Advances in statistical multisource-multitarget information fusion:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston
Artech House
[2014]
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| Schriftenreihe: | Artech House electronic warfare library
|
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 UER01 |
| Beschreibung: | 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 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 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 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 |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781608077991 1608077993 1608077985 9781608077984 |
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| 100 | 1 | |a Mahler, Ronald P. S. |e Verfasser |0 (DE-588)1060136198 |4 aut | |
| 245 | 1 | 0 | |a Advances in statistical multisource-multitarget information fusion |c Ronald P.S. Mahler |
| 264 | 1 | |a Boston |b Artech House |c [2014] | |
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| 500 | |a Includes bibliographical references and index | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 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 | ||
| 500 | |a 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 | ||
| 650 | 7 | |a COMPUTERS / General |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Expert systems (Computer science) |2 fast | |
| 650 | 7 | |a Multisensor data fusion / Mathematics |2 fast | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Expert systems (Computer science) | |
| 650 | 4 | |a Multisensor data fusion |x Mathematics | |
| 650 | 4 | |a Bayesian statistical decision theory | |
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Datensatz im Suchindex
| _version_ | 1804176918075932672 |
|---|---|
| any_adam_object | |
| author | Mahler, Ronald P. S. |
| author_GND | (DE-588)1060136198 |
| author_facet | Mahler, Ronald P. S. |
| author_role | aut |
| author_sort | Mahler, Ronald P. S. |
| author_variant | r p s m rps rpsm |
| building | Verbundindex |
| bvnumber | BV043960853 |
| collection | ZDB-4-EBA |
| ctrlnum | (ZDB-4-EBA)ocn935983055 (OCoLC)935983055 (DE-599)BVBBV043960853 |
| dewey-full | 006.3/3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/3 |
| dewey-search | 006.3/3 |
| dewey-sort | 16.3 13 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV043960853 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:48Z |
| institution | BVB |
| isbn | 9781608077991 1608077993 1608077985 9781608077984 |
| language | English |
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| record_format | marc |
| series2 | Artech House electronic warfare library |
| spelling | Mahler, Ronald P. S. Verfasser (DE-588)1060136198 aut Advances in statistical multisource-multitarget information fusion Ronald P.S. Mahler Boston Artech House [2014] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Artech House electronic warfare library 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 10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 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 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 COMPUTERS / General bisacsh Bayesian statistical decision theory fast Expert systems (Computer science) fast Multisensor data fusion / Mathematics fast Mathematik Expert systems (Computer science) Multisensor data fusion Mathematics Bayesian statistical decision theory |
| spellingShingle | Mahler, Ronald P. S. Advances in statistical multisource-multitarget information fusion COMPUTERS / General bisacsh Bayesian statistical decision theory fast Expert systems (Computer science) fast Multisensor data fusion / Mathematics fast Mathematik Expert systems (Computer science) Multisensor data fusion Mathematics Bayesian statistical decision theory |
| title | Advances in statistical multisource-multitarget information fusion |
| title_auth | Advances in statistical multisource-multitarget information fusion |
| title_exact_search | Advances in statistical multisource-multitarget information fusion |
| title_full | Advances in statistical multisource-multitarget information fusion Ronald P.S. Mahler |
| title_fullStr | Advances in statistical multisource-multitarget information fusion Ronald P.S. Mahler |
| title_full_unstemmed | Advances in statistical multisource-multitarget information fusion Ronald P.S. Mahler |
| title_short | Advances in statistical multisource-multitarget information fusion |
| title_sort | advances in statistical multisource multitarget information fusion |
| topic | COMPUTERS / General bisacsh Bayesian statistical decision theory fast Expert systems (Computer science) fast Multisensor data fusion / Mathematics fast Mathematik Expert systems (Computer science) Multisensor data fusion Mathematics Bayesian statistical decision theory |
| topic_facet | COMPUTERS / General Bayesian statistical decision theory Expert systems (Computer science) Multisensor data fusion / Mathematics Mathematik Multisensor data fusion Mathematics |
| work_keys_str_mv | AT mahlerronaldps advancesinstatisticalmultisourcemultitargetinformationfusion |