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    A Note on the Reward Function for PHD Filters with Sensor Control

    Access Status
    Fulltext not available
    Authors
    Ristic, B.
    Vo, Ba-Ngu
    Clark, D.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Ristic, B. and Vo, B. and Clark, D. 2011. A Note on the Reward Function for PHD Filters with Sensor Control. IEEE Transactions on Aerospace and Electronic Systems. 47 (2): pp. 1521-1529.
    Source Title
    IEEE Transactions on Aerospace and Electronic Systems
    DOI
    10.1109/TAES.2011.5751278
    ISSN
    0018-9251
    URI
    http://hdl.handle.net/20.500.11937/30999
    Collection
    • Curtin Research Publications
    Abstract

    The context is sensor control for multi-object Bayes filtering in the framework of partially observed Markov decision processes (POMDPs). The current information state is represented by the multi-object probability density function (pdf), while the reward function associated with each sensor control (action) is the information gain measured by the alpha or Rényi divergence. Assuming that both the predicted and updated state can be represented by independent identically distributed (IID) cluster random finite sets (RFSs) or, as a special case, the Poisson RFSs, this work derives the analytic expressions of the corresponding Rényi divergence based information gains. The implementation of Rényi divergence via the sequential Monte Carlo method is presented. The performance of the proposed reward function is demonstrated by a numerical example, where a moving range-only sensor is controlled to estimate the number and the states of several moving objects using the PHD filter.

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