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Particle filters for the estimation of a state space model

Lookup NU author(s): Tao Chen, Emeritus Professor Julian Morris, Professor Elaine Martin


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In process engineering, on-line state estimation is a key element in state space modelling. However when state/measurement functions are highly non-linear and the posterior probability of the state is non-Gaussian, conventional filters, such as the extended Kalman filter, do not provide satisfactory results. This paper proposes an alternative approach whereby particle filters based on the sequential Monte Carlo method are used for the estimation task. Particle filters are described prior to discussing a number of implementation issues. Furthermore, a Markov chain Monte Carlo method is proposed to enhance particle filters where the estimates of the initial conditions are poor. The effectiveness of particle filters is demonstrated through the state estimation of an exothermic irreversible parallel reaction in a continuous stirred tank reactor. © 2004 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Chen T, Morris J, Martin E

Publication type: Article

Publication status: Published

Journal: Computer Aided Chemical Engineering

Year: 2004

Volume: 18

Issue: C

Pages: 613-618

ISSN (print): 1570-7946

ISSN (electronic):

Publisher: Elsevier BV


DOI: 10.1016/S1570-7946(04)80168-8


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