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005 20160805110913.3
006 m o d
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008 160125s2014 mau ob 001 0 eng d
019 949847062
020 9781608077991|qelectronic book
020 1608077993|qelectronic book
020 |z1608077985
020 |z9781608077984
035 (OCoLC)935983055|z(OCoLC)949847062
040 YDXCP|beng|cYDXCP|dOCLCO|dN$T|dOCLCF|dEBLCP
049 RIDW
050 4 QA76.76.E95|bM343 2014
072 7 COM|x000000|2bisacsh
082 04 006.3/3|223
090 QA76.76.E95|bM343 2014
100 1 Mahler, Ronald P. S.|0https://id.loc.gov/authorities/names
/n97063006
245 00 Advances in statistical multisource-multitarget
information fusion /|cRonald P.S. Mahler.
264 1 Boston :|bArtech House,|c[2014]
264 4 |c©2014
300 1 online resource.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|2rdaft
490 1 Artech House electronic warfare library
504 Includes bibliographical references and index.
505 0 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
505 8 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
505 8 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
505 8 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
505 8 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
520 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.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Expert systems (Computer science)|0https://id.loc.gov/
authorities/subjects/sh85046450
650 0 Multisensor data fusion|0https://id.loc.gov/authorities/
subjects/sh90003105|xMathematics.|0https://id.loc.gov/
authorities/subjects/sh2002007922
650 0 Bayesian statistical decision theory.|0https://id.loc.gov/
authorities/subjects/sh85012506
650 7 Expert systems (Computer science)|2fast|0https://
id.worldcat.org/fast/918516
650 7 Multisensor data fusion|xMathematics.|2fast|0https://
id.worldcat.org/fast/1029097
650 7 Multisensor data fusion.|2fast|0https://id.worldcat.org/
fast/1029095
650 7 Bayesian statistical decision theory.|2fast|0https://
id.worldcat.org/fast/829019
655 0 Electronic books.
655 4 Electronic books.
776 08 |iPrint version:|z1608077985|z9781608077984
|w(OCoLC)876671549
830 0 Artech House electronic warfare library.|0https://
id.loc.gov/authorities/names/no2010127357
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=1155213|zOnline eBook. Access restricted to
current Rider University students, faculty, and staff.
856 42 |3Instructions for reading/downloading this eBook|uhttp://
guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20161013|cEBSCO|tebscoebooksacademic new |lridw
994 92|bRID
