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    Sensor Control for Multi-Object State-Space Estimation Using Random Finite Sets

    Access Status
    Fulltext not available
    Authors
    Ristic, B.
    Vo, Ba-Ngu
    Date
    2010
    Type
    Journal Article
    
    Metadata
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    Citation
    Ristic, B. and Vo, B. 2010. Sensor Control for Multi-Object State-Space Estimation Using Random Finite Sets. Automatica. 46 (11): pp. 1812-1818.
    Source Title
    Automatica
    DOI
    10.1016/j.automatica.2010.06.045
    ISSN
    0005-1098
    URI
    http://hdl.handle.net/20.500.11937/48253
    Collection
    • Curtin Research Publications
    Abstract

    The problem addressed in this paper is information theoretic sensor control for recursive Bayesian multi-object state-space estimation using random finite sets. The proposed algorithm is formulated in the framework of partially observed Markov decision processes where the reward function associated with different sensor actions is computed via the Renyi or alpha divergence between the multi-object prior and the multi-object posterior densities. The proposed algorithm in implemented via the sequential Monte Carlo method. The paper then presents a case study where the problem is to localise an unknown number of sources using a controllable moving sensor which provides range-only detections. Four sensor control reward functions are compared in the study and the proposed scheme is found to perform the best.

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