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    Information theoretic quantiser design for decentralised estimation of Hidden Markov Models


    Dey, Subhrakanti and Galati, F. Antonio (2003) Information theoretic quantiser design for decentralised estimation of Hidden Markov Models. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). IEEE. ISBN 0780376633

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    Abstract

    Quantiser design for a nonlinear filter is considered in the context of a decentralised estimation system with communication constraints. The filter is based on the quantised outputs of a discrete-time, two-state hidden Markov model (HMM) as measured by two remote sensor nodes. The optimal quantisation scheme is obtained by maximising the mutual information between the quantised measurements and the hidden Markov states. Filter performance is measured in terms of the probability of estimation error and is investigated through simulation for HMMs with both independent and correlated white Gaussian noise in the measurements. The performance of the filter based on continuous, unquantised signals provides a benchmark for the performance of the filter based on quantised measurements. Therefore, a method for computing the probability of estimation error directly for the continuous filter is also presented.

    Item Type: Book Section
    Additional Information: Cite as: S. Dey and F. A. Galati, "Information theoretic quantiser design for decentralised estimation of hidden Markov models," 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003, pp. VI-749, doi: 10.1109/ICASSP.2003.1201790.
    Keywords: Information; theoretic; quantiser; design; decentralised; estimation; hidden; Markov models;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14451
    Identification Number: https://doi.org/10.1109/ICASSP.2003.1201790
    Depositing User: Subhrakanti Dey
    Date Deposited: 24 May 2021 15:55
    Publisher: IEEE
    Refereed: Yes
    URI:

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