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dc.contributor.authorBeard, M.
dc.contributor.authorReuter, S.
dc.contributor.authorGranström, K.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorScheel, A.
dc.date.accessioned2017-01-30T14:38:30Z
dc.date.available2017-01-30T14:38:30Z
dc.date.created2016-04-26T19:30:23Z
dc.date.issued2015
dc.identifier.citationBeard, M. and Reuter, S. and Granström, K. and Vo, B. and Vo, B.T. and Scheel, A. 2015. A generalised labelled multi-Bernoulli filter for extended multi-target tracking, in Proceedings of the 18th International Conference on Information Fusion (Fusion), Jul 6-9 2015, pp. 991-998. Washington, DC: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39950
dc.description.abstract

This paper addresses extended multi-target tracking in clutter, i.e. tracking targets that may produce more than one measurement on each scan. We propose a new algorithm for solving this problem, that is capable of initiating and maintaining labelled estimates of the target kinematics, measurement rates and extents. Our proposed technique is based on modelling the multi-target state as a generalised labelled multi-Bernoulli (GLMB), combined with the gamma Gaussian inverse Wishart (GGIW) distribution for a single extended target. Previously, probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters based on GGIW mixtures have been proposed to solve the extended target tracking problem. Although these are computationally cheaper, they involve significant approximations, as well as lacking the ability to maintain target tracks over time. Here, we compare our proposed GLMB-based approach to the extended target PHD/CPHD filters, and show that the GLMB has improved performance.

dc.titleA generalised labelled multi-Bernoulli filter for extended multi-target tracking
dc.typeConference Paper
dcterms.source.startPage991
dcterms.source.endPage998
dcterms.source.title2015 18th International Conference on Information Fusion, Fusion 2015
dcterms.source.series2015 18th International Conference on Information Fusion, Fusion 2015
dcterms.source.isbn9780982443866
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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