Low-delay optimal MAP state estimation in HMM's with application to symbol decoding

Moon Seo Park, David J. Miller

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    A new algorithm is developed for realizing optimal maximum a posteriori (MAP) estimates of the hidden states associated with a hidden Markov model, given a sequence of observed symbols. The standard MAP algorithm of Bahl et al., requires direct calculation of the a posteriori probabilities using the forward/backward algorithm, with each state estimate based on the entire observation sequence. For decoding applications, this implies huge, practically infinite delay. The new algorithm finds the optimal MAP estimate without directly computing the a posteriori probabilities and is a variable delay method that typically achieves a small average delay. The method is applied, in comparison with known techniques, to the problem of source decoding over noisy channels.

    Original languageEnglish (US)
    Pages (from-to)289-292
    Number of pages4
    JournalIEEE Signal Processing Letters
    Volume4
    Issue number10
    DOIs
    StatePublished - Dec 1 1997

    Fingerprint

    Maximum a Posteriori Estimation
    Maximum a Posteriori
    State Estimation
    State estimation
    Decoding
    A Posteriori Estimates
    Forward-backward Algorithm
    Variable Delay
    Infinite Delay
    Markov Model
    Hidden Markov models
    Entire
    Imply
    Computing
    Estimate

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics

    Cite this

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    Low-delay optimal MAP state estimation in HMM's with application to symbol decoding. / Park, Moon Seo; Miller, David J.

    In: IEEE Signal Processing Letters, Vol. 4, No. 10, 01.12.1997, p. 289-292.

    Research output: Contribution to journalArticle

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