MURAL - Maynooth University Research Archive Library



    Reduced-complexity filtering for partially observed nearly completely decomposable Markov chains


    Dey, Subhrakanti (2000) Reduced-complexity filtering for partially observed nearly completely decomposable Markov chains. IEEE Transactions on Signal Processing, 48 (12). pp. 3334-3344. ISSN 1053-587X

    [img]
    Preview
    Download (284kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    This paper provides a systematic method of obtaining reduced-complexity approximations to aggregate filters for a class of partially observed nearly completely decomposable Markov chains. It is also shown why an aggregate filter adapted from Courtois' (1977) aggregation scheme has the same order of approximation as achieved by the algorithm proposed in this paper. This algorithm can also be used systematically to obtain reduced-complexity approximations to the full-order fitter as opposed to algorithms adapted from other aggregation schemes. However, the computational savings in computing the full-order filters are substantial only when the large scale Markov chain has a large number of weakly interacting blocks or "superstates" with small individual dimensions. Some simulations are carried out to compare the performance of our algorithm with algorithms adapted from various other aggregation schemes on the basis of an average approximation error criterion in aggregate (slow) filtering. These studies indicate that the algorithms adapted from other aggregation schemes may become ad hoc under certain circumstances. The algorithm proposed in this paper however, always yields reduced-complexity filters with a guaranteed order of approximation by appropriately exploiting the special structures of the system matrices.

    Item Type: Article
    Keywords: Hidden Markov models; queuing analysis; reduced-order systems; singularly perturbed systems; state estimation;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14410
    Identification Number: https://doi.org/10.1109/78.886997
    Depositing User: Subhrakanti Dey
    Date Deposited: 10 May 2021 15:51
    Journal or Publication Title: IEEE Transactions on Signal Processing
    Publisher: Institute of Electrical and Electronics Engineers
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year