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dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.identifier.citationVo, B.T. and Vo, B. 2018. Multi-Scan Generalized Labeled Multi-Bernoulli Filter, pp. 195-202.

© 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density. The GLMB filter is an analytic solution to the labeled multi-target filtering recursion. In this work, we show that the GLMB filter can be extended to an analytic multi-object posterior recursion.

dc.titleMulti-Scan Generalized Labeled Multi-Bernoulli Filter
dc.typeConference Paper
dcterms.source.title2018 21st International Conference on Information Fusion, FUSION 2018
dcterms.source.series2018 21st International Conference on Information Fusion, FUSION 2018
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available

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