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Bayesian State and Parameter Estimation of Uncertain Dynamical Systems

Ching, Jianye and Beck, James L. and Porter, Keith A. (2006) Bayesian State and Parameter Estimation of Uncertain Dynamical Systems. Probabilistic Engineering Mechanics, 21 (1). pp. 81-96. ISSN 0266-8920. doi:10.1016/j.probengmech.2005.08.003.

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The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the particle filter simulations are introduced and discussed. Comparisons between the particle filter and the extended Kalman filter are made using several numerical examples of nonlinear systems. The results indicate that the particle filter provides consistent state and parameter estimates for highly nonlinear models, while the extended Kalman filter does not.

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Additional Information:© 2005 Elsevier Ltd. All rights reserved. Received 4 August 2004; received in revised form 16 May 2005; accepted 6 August 2005. Available online 4 November 2005.
Subject Keywords:Bayesian analysis; State estimation; Parameter estimation; Dynamical systems; Monte Carlo simulation; Importance sampling; Particle filter; Stochastic simulation; Extended Kalman filter
Issue or Number:1
Record Number:CaltechAUTHORS:20120817-163453700
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:33331
Deposited By: Carmen Nemer-Sirois
Deposited On:20 Aug 2012 22:48
Last Modified:09 Nov 2021 21:34

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