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    Risk-sensitive filtering and smoothing for hidden Markov models


    Dey, Subhrakanti and Moore, John B. (1995) Risk-sensitive filtering and smoothing for hidden Markov models. Systems & Control Letters, 25 (5). pp. 361-366. ISSN 0167-6911

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    Abstract

    In this paper, we address the problem of risk-sensitive filtering and smoothing for discrete-time Hidden Markov Models (HMM) with finite-discrete states. The objective of risk-sensitive filtering is to minimise the expectation of the exponential of the squared estimation error weighted by a risk-sensitive parameter. We use the so-called Reference Probability Method in solving this problem. We achieve finite-dimensional linear recursions in the information state, and thereby the state estimate that minimises the risk-sensitive cost index. Also, fixed-interval smoothing results are derived. We show that L2 or risk-neutral filtering for HMMs can be extracted as a limiting case of the risk-sensitive filtering problem when the risk-sensitive parameter approaches zero.

    Item Type: Article
    Keywords: Hidden Markov model; Risk-sensitive filtering; Information state; Fixed-interval smoothing;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14402
    Identification Number: https://doi.org/10.1016/0167-6911(94)00093-B
    Depositing User: Subhrakanti Dey
    Date Deposited: 10 May 2021 13:45
    Journal or Publication Title: Systems & Control Letters
    Publisher: Elsevier
    Refereed: Yes
    URI:

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