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dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorPhung, D.
dc.date.accessioned2017-01-30T11:03:12Z
dc.date.available2017-01-30T11:03:12Z
dc.date.created2015-04-09T09:14:31Z
dc.date.issued2014
dc.date.submitted2015-04-09
dc.identifier.citationVo, B. and Vo, B.T. and Phung, D. 2014. Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter. IEEE Transactions on Signal Processing. 62 (24): pp. 6554-6567.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/7899
dc.identifier.doi10.1109/TSP.2014.2364014
dc.description.abstract

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.

dc.publisherIEEE
dc.titleLabeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
dc.typeJournal Article
dcterms.dateSubmitted2015-04-09
dcterms.source.volume62
dcterms.source.number24
dcterms.source.startPage6554
dcterms.source.endPage6567
dcterms.source.issn1053-587X
dcterms.source.titleIEEE Transactions on Signal Processing
curtin.digitool.pid225298
curtin.pubStatusPublished
curtin.refereedTRUE
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.identifier.scriptidPUB-SE-DEC-BTV-87273
curtin.accessStatusFulltext not available


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