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    Bayesian Filtering With Random Finite Set Observations

    200126_200126 AFD.pdf (896.5Kb)
    Access Status
    Open access
    Authors
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Cantoni, Antonio
    Date
    2008
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Vo, B.T. and Vo, B. and Cantoni, A. 2008. Bayesian Filtering With Random Finite Set Observations. IEEE Transactions on Signal Processing. 56 (4): pp. 1313-1326.
    Source Title
    IEEE Trans on Signal Processing
    DOI
    10.1109/TSP.2007.908968
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    Remarks

    Copyright © 2008. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/14299
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a novel and mathematically rigorous Bayes’ recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes’ recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptions, an exact closed-form solution to the proposed recursion is derived, and efficient implementations are given. Extensions of the closed-form recursion to accommodate mild nonlinearities are also given using linearization and unscented transforms.

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