Lancaster EPrints

Particle Learning Methods for State and Parameter Estimation

Nemeth, Christopher and Fearnhead, Paul and Mihaylova, Lyudmila and Vorley, D. (2012) Particle Learning Methods for State and Parameter Estimation. In: Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. UNSPECIFIED. ISBN 978-1-84919-624-6

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    Abstract

    This paper presents an approach for online parameter estimation within particle lters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuver- ability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation of non-static parameters which change at some unknown point in time. Our parameter estimation is updated using changepoint analysis, where a changepoint is identied when a signicant change occurs in the observations of the system, such as changes in direction or velocity.

    Item Type: Contribution in Book/Report/Proceedings
    Uncontrolled Keywords: parameter estimation ; Monte Carlo methods ; nonlinear filtering ; changepoint detection
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    Faculty of Science and Technology > Mathematics and Statistics
    ID Code: 54410
    Deposited By: ep_importer_pure
    Deposited On: 22 May 2012 10:16
    Refereed?: No
    Published?: Published
    Last Modified: 09 Oct 2013 15:42
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/54410

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