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    A Tutorial on Bernoulli Filters: Theory, Implementation and Applications

    Access Status
    Fulltext not available
    Authors
    Ristic, B.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Farina, A.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Ristic, Branko and Vo, Ba-Tuong and Vo, Ba-Ngu and Farina, Alfonso. 2013. A Tutorial on Bernoulli Filters: Theory, Implementation and Applications. IEEE Transactions on Signal Processing. 61 (13): pp. 3406-3430.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2013.2257765
    ISSN
    1053-587X
    URI
    http://hdl.handle.net/20.500.11937/6652
    Collection
    • Curtin Research Publications
    Abstract

    Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly switch on and off. The applications are primarily in target tracking, where the switching process models target appearance or disappearance from the surveillance volume. The concept, however, is applicable to a range of dynamic phenomena, such as epidemics, pollution, social trends, etc. Bernoulli filters in general have no analytic solution and are implemented as particle filters or Gaussian sum filters. This tutorial paper reviews the theory of Bernoulli filters as well as their implementation for different measurement models. The theory is backed up by applications in sensor networks, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multi-target detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading.

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