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dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorVo, Ba Tuong
dc.date.accessioned2018-02-06T06:15:45Z
dc.date.available2018-02-06T06:15:45Z
dc.date.created2018-02-06T05:49:57Z
dc.date.issued2017
dc.identifier.citationVo, B. and Vo, B.T. 2017. An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/63168
dc.identifier.doi10.23919/ICIF.2017.8009647
dc.description.abstract

© 2017 International Society of Information Fusion (ISIF). This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a complexity in the order of the product of the number of measurements from each sensor, and quadratic in the number of hypothesized objects.

dc.titleAn implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
dc.typeConference Paper
dcterms.source.title20th International Conference on Information Fusion, Fusion 2017 - Proceedings
dcterms.source.series20th International Conference on Information Fusion, Fusion 2017 - Proceedings
dcterms.source.isbn9780996452700
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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