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    On CPHD filters with track labeling

    Access Status
    Fulltext not available
    Authors
    Mahler, Ronald
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Mahler, R. 2017. On CPHD filters with track labeling.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    DOI
    10.1117/12.2263508
    ISBN
    9781510609013
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55814
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 SPIE. The random infinite set (RFS) approach to information fusion addressed target track-labeling from the outset. The first implementations of RFS filters did not do so because of computational concerns, whereas subsequent implementations employed heuristics. The labeled RFS (LRFS) theory of B.-T. Vo and B.-N. Vo was the first systematic, theoretically rigorous formulation of true multitarget tracking; and led to the generalized labeled multi-Bernoulli (GLMB) filter (the first provably Bayes-optimal multitarget tracking algorithm). This paper addresses the feasibility of theoretically rigorous cardinalized probability hypothesis density (CPHD) filters. We show that an approximation of the GLMB filter, known as the LMB filter, can be reinterpreted as a theoretically rigorous labeled PHD (LPHD) filter. We also prove two characterization theorems for the probability generating functionals (p.g.fl's) of LRFS's.

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