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    Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models

    Access Status
    Fulltext not available
    Authors
    Beard, Michael
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Arulampalam, S.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Beard, M. and Vo, B.T. and Vo, B. and Arulampalam, S. 2017. Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models. IEEE Transactions on Signal Processing.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2017.2723355
    ISSN
    1053-587X
    School
    School of Electrical Engineering and Computing
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP130104404
    URI
    http://hdl.handle.net/20.500.11937/56063
    Collection
    • Curtin Research Publications
    Abstract

    Crown The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy-Schwarz divergence for GLMBs. The proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a GLMB, while the proposed analytic Cauchy-Schwarz divergence provides a tractable measure of similarity between GLMBs. We demonstrate the use of both results on a partially observed Markov decision process for GLMBs, with Cauchy-Schwarz divergence based reward, and void probability constraint.

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