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    Multi-Scan Generalized Labeled Multi-Bernoulli Filter

    Access Status
    Fulltext not available
    Authors
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2018
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    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
    DOI
    10.23919/ICIF.2018.8455419
    ISBN
    9780996452762
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP170104854
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/71807
    Collection
    • Curtin Research Publications
    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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