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dc.contributor.authorBeard, M.
dc.contributor.authorVo, Ba Tuong
dc.contributor.authorVo, Ba-Ngu
dc.identifier.citationBeard, M. and Vo, B.T. and Vo, B. 2016. Generalised labelled multi-Bernoulli forward-backward smoothing, in Proceedings of the 19th International Conference on Information Fusion, Jul 5-8 2016, pp. 688-694. Heidelberg, Germany: IEEE.

This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections.

dc.titleGeneralised labelled multi-Bernoulli forward-backward smoothing
dc.typeConference Paper
dcterms.source.titleFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
dcterms.source.seriesFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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