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Particle filters for the estimation of a state space model

Lookup NU author(s): 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.

Publication metadata

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

Editor(s): BarbosaPovoa, A.P., Matos, H.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 14th European Symposium on Computer Aided Process Engineering (ESCAPE)

Year of Conference: 2004

Pages: 613-618

ISSN: 1570-7946

Publisher: Elsevier BV

Library holdings: Search Newcastle University Library for this item

ISBN: 9780444516947