Multi-Scan Generalized Labeled Multi-Bernoulli Filter
Access Status
Fulltext not available
Authors
Vo, Ba Tuong
Vo, Ba-Ngu
Date
2018Type
Conference Paper
Metadata
Show full item recordCitation
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)
Funding and Sponsorship
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.
Related items
Showing items related by title, author, creator and subject.
-
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 ...
-
Jones, B.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and ...
-
Papi, Francesco; Kim, Du Yong (2015)In this paper we present a general solution for multi-target tracking with superpositional measurements. Measurements that are functions of the sum of the contributions of the targets present in the surveillance area are ...