Particle filter based MAP state estimation: A comparison

S. Saha, Y. Boers, J.N. Driessen, Pranab K. Mandal, Arunabha Bagchi

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    28 Citations (Scopus)
    116 Downloads (Pure)


    MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi algorithm based MAP sequence estimator has been developed. In this paper, we compare these two methods for estimating the current state and the numerical results show that the former performs better.
    Original languageUndefined
    Title of host publicationProceedings of 12th International Conference on Information Fusion 2009
    PublisherInternational Society of Information Fusion
    Number of pages6
    ISBN (Print)978-0-9824438-0-4
    Publication statusPublished - 6 Jul 2009
    Event12th International Conference on Information Fusion, FUSION 2009 - Seattle, United States
    Duration: 6 Jul 20099 Jul 2009
    Conference number: 12

    Publication series

    PublisherInternational Society of Information Fusion


    Conference12th International Conference on Information Fusion, FUSION 2009
    Abbreviated titleFUSION 2009
    Country/TerritoryUnited States


    • METIS-264049
    • Filter MAP
    • Bayesian point estimation
    • MSC-11K45
    • Particle filter
    • Sequential Monte Carlo
    • EWI-16113
    • IR-68153

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