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Target tracking with random finite sets

Wu, Weihua.

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Target tracking with random finite sets

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Target tracking with random finite sets/ by Weihua Wu ... [et al.].
其他作者:	Wu, Weihua.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xvi, 443 p. :ill., digital ;24 cm.
內容註:	Introduction -- Single Target Tracking Algorithms -- Foundation of the Random Finite Set -- The Probability Hypothesis Density Filter -- The Cardinalized Probability Hypothesis Density Filter -- The Multi-Bernoulli Filter -- Labeled RFS-Based Filters -- Maneuvering Target Tracking -- Target Tracking for Doppler Radars -- Track-Before-Detect for Dim Targets -- Target Tracking With Non-Standard Measurements -- Target Tracking for Multiple Distributed Sensors -- Appendix.
Contained By:	Springer Nature eBook
標題:	Random sets. -
電子資源:	https://doi.org/10.1007/978-981-19-9815-7
ISBN:	9789811998157

Target tracking with random finite sets
Target tracking with random finite sets[electronic resource] /by Weihua Wu ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xvi, 443 p. :ill., digital ;24 cm.

Introduction -- Single Target Tracking Algorithms -- Foundation of the Random Finite Set -- The Probability Hypothesis Density Filter -- The Cardinalized Probability Hypothesis Density Filter -- The Multi-Bernoulli Filter -- Labeled RFS-Based Filters -- Maneuvering Target Tracking -- Target Tracking for Doppler Radars -- Track-Before-Detect for Dim Targets -- Target Tracking With Non-Standard Measurements -- Target Tracking for Multiple Distributed Sensors -- Appendix.

This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.

ISBN: 9789811998157

Standard No.: 10.1007/978-981-19-9815-7doiSubjects--Topical Terms:

752988
Random sets.

LC Class. No.: TK6580

Dewey Class. No.: 621.3848

Target tracking with random finite sets
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505 0 $a Introduction -- Single Target Tracking Algorithms -- Foundation of the Random Finite Set -- The Probability Hypothesis Density Filter -- The Cardinalized Probability Hypothesis Density Filter -- The Multi-Bernoulli Filter -- Labeled RFS-Based Filters -- Maneuvering Target Tracking -- Target Tracking for Doppler Radars -- Track-Before-Detect for Dim Targets -- Target Tracking With Non-Standard Measurements -- Target Tracking for Multiple Distributed Sensors -- Appendix.
520 $a This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
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