Show simple item record

dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.date.accessioned2018-12-13T09:11:24Z
dc.date.available2018-12-13T09:11:24Z
dc.date.created2018-12-12T02:46:41Z
dc.date.issued2018
dc.identifier.citationVo, B.T. and Vo, B. 2018. Multi-Scan Generalized Labeled Multi-Bernoulli Filter, pp. 195-202.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/71807
dc.identifier.doi10.23919/ICIF.2018.8455419
dc.description.abstract

© 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.startPage195
dcterms.source.endPage202
dcterms.source.title2018 21st International Conference on Information Fusion, FUSION 2018
dcterms.source.series2018 21st International Conference on Information Fusion, FUSION 2018
dcterms.source.isbn9780996452762
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record