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A canonical space-time state space model: state and parameter estimation

Dewar, M. and Kadirkarnanathan, V. (2007) A canonical space-time state space model: state and parameter estimation. IEEE Transactions on Signal Processing, 55 (10). pp. 4862-4870. ISSN 1053-587X


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The maximum likelihood estimation of a dynamic spatiotemporal model is introduced, centred around the inclusion of a prior arbitrary spatiotemporal neighborhood description. The neighborhood description defines a specific parameterization of the state transition matrix, chosen on the basis of prior knowledge about the system. The model used is inspired by the spatiotemporal ARMA (STARMA) model, but the representation used is based on the standard state-space model. The inclusion of the neighborhood into an expectation-maximization based joint state and parameter estimation algorithm allows for accurate characterization of the spatiotemporal model. The process of including the neighborhood, and the effect it has on the maximum likelihood parameter estimate is described and demonstrated in this paper.

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 05 Dec 2007 17:34
Last Modified: 16 Jun 2014 06:40
Published Version: http://dx.doi.org/10.1109/TSP.2007.896245
Status: Published
Publisher: IEEE-INST Electrical Electronics Engineers Inc.
Refereed: Yes
Identification Number: 10.1109/TSP.2007.896245
URI: http://eprints.whiterose.ac.uk/id/eprint/3466

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