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dc.contributor.authorBryant, D.
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorJones, B.
dc.date.accessioned2018-05-18T08:00:59Z
dc.date.available2018-05-18T08:00:59Z
dc.date.created2018-05-18T00:23:04Z
dc.date.issued2017
dc.identifier.citationBryant, D. and Vo, B.T. and Vo, B. and Jones, B. 2017. A labeled random finite set spawning model, pp. 215-220.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/68138
dc.identifier.doi10.1109/ICCAIS.2017.8217579
dc.description.abstract

© 2017 IEEE. Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage.

dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP170104854
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP160104662
dc.titleA labeled random finite set spawning model
dc.typeConference Paper
dcterms.source.volume2017-January
dcterms.source.startPage215
dcterms.source.endPage220
dcterms.source.title2017 International Conference on Control, Automation and Information Sciences, ICCAIS 2017
dcterms.source.series2017 International Conference on Control, Automation and Information Sciences, ICCAIS 2017
dcterms.source.isbn9781538631140
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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