Multi-Scan Generalized Labeled Multi-Bernoulli Filter
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Authors
Vo, Ba Tuong
Vo, Ba-Ngu
Date
2018Type
Conference Paper
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Vo, B.T. and Vo, B. 2018. Multi-Scan Generalized Labeled Multi-Bernoulli Filter, pp. 195-202.
Source Title
2018 21st International Conference on Information Fusion, FUSION 2018
ISBN
School
School of Electrical Engineering, Computing and Mathematical Science (EECMS)
Collection
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.
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Vo, Ba-Ngu; Vo, Ba Tuong; Phung, D. (2014)An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate ...
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