SMC methods to avoid self-resolving for online Bayesian parameter estimation

E.H. Aoki, Y. Boers, Pranab K. Mandal, Arunabha Bagchi

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    Abstract

    The particle filter is a powerful filtering technique that is able to handle a broad scope of nonlinear problems. However, it has also limitations: a standard particle filter is unable to handle, for instance, systems that include static variables (parameters) to be estimated together with the dynamic states. This limitation is due to the well-known “self-resolving‿ phenomenon, which is caused by the gradual loss of information that occurs during the resampling steps. In the context of online Bayesian parameter estimation, some approaches to handle this problem have proposed, such as adding artificial dynamics to the parameter model. However, these approaches typically both introduce new parameters (e.g. the intensity of artificial process noise) and inherent biases to the estimation problem. In this paper, we will give a give a look at two Sequential Monte Carlo techniques that do not rely on biasing the system model: the Autonomous Multiple Model particle filter and the Rao-Blackwellized Marginal particle filter. These approaches are not new, but have not been applied yet to the problem of online Bayesian parameter estimation for non-structured models. We will derive suitable adaptations of these methods for this problem and evaluate them using simulations.
    Original languageUndefined
    Title of host publicationProceedings of the 15th International Conference on Information Fusion (FUSION 2012)
    Place of PublicationSingapore
    PublisherIEEE
    Pages98-105
    Number of pages8
    ISBN (Print)978-1-4673-0417-7
    Publication statusPublished - Jul 2012

    Publication series

    Name
    PublisherIEEE Xplore

    Keywords

    • EWI-22620
    • IR-82547
    • METIS-296161

    Cite this

    Aoki, E. H., Boers, Y., Mandal, P. K., & Bagchi, A. (2012). SMC methods to avoid self-resolving for online Bayesian parameter estimation. In Proceedings of the 15th International Conference on Information Fusion (FUSION 2012) (pp. 98-105). Singapore: IEEE.