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dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorCantoni, Antonio
dc.date.accessioned2017-01-30T11:42:49Z
dc.date.available2017-01-30T11:42:49Z
dc.date.created2014-08-19T20:00:29Z
dc.date.issued2008
dc.identifier.citationVo, B.T. and Vo, B. and Cantoni, A. 2008. Bayesian Filtering With Random Finite Set Observations. IEEE Transactions on Signal Processing. 56 (4): pp. 1313-1326.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/14299
dc.identifier.doi10.1109/TSP.2007.908968
dc.description.abstract

This paper presents a novel and mathematically rigorous Bayes’ recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes’ recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptions, an exact closed-form solution to the proposed recursion is derived, and efficient implementations are given. Extensions of the closed-form recursion to accommodate mild nonlinearities are also given using linearization and unscented transforms.

dc.publisherIEEE
dc.subjectPHD filter
dc.subjectKalman filter
dc.subjecttarget tracking
dc.subjectpoint processes
dc.subjectGaussian sum filter
dc.subjectCPHD filter
dc.subjectparticle filter
dc.subjectBayesian filtering
dc.subjectrandom finite sets
dc.titleBayesian Filtering With Random Finite Set Observations
dc.typeJournal Article
dcterms.source.volume56
dcterms.source.number4
dcterms.source.startPage1313
dcterms.source.endPage1326
dcterms.source.issn1053-587X
dcterms.source.titleIEEE Trans on Signal Processing
curtin.note

Copyright © 2008. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access


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