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Particle Filtering With Dependent Noise Processes
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 9, 4497-4508 p.Article in journal (Refereed) Published
Abstract [en]

Modeling physical systems often leads to discrete time state-space models with dependent process and measurement noises. For linear Gaussian models, the Kalman filter handles this case, as is well described in literature. However, for nonlinear or non-Gaussian models, the particle filter as described in literature provides a general solution only for the case of independent noise. Here, we present an extended theory of the particle filter for dependent noises with the following key contributions: i) The optimal proposal distribution is derived; ii) the special case of Gaussian noise in nonlinear models is treated in detail, leading to a concrete algorithm that is as easy to implement as the corresponding Kalman filter; iii) the marginalized (Rao-Blackwellized) particle filter, handling linear Gaussian substructures in the model in an efficient way, is extended to dependent noise; and, finally, iv) the parameters of a joint Gaussian distribution of the noise processes are estimated jointly with the state in a recursive way.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2012. Vol. 60, no 9, 4497-4508 p.
Keyword [en]
Bayesian methods, Dependent noise, Particle filters, Rao–Blackwellized particle filter, Recursive estimation
National Category
Signal Processing Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-79599DOI: 10.1109/TSP.2012.2202653ISI: 000307790800001OAI: oai:DiVA.org:liu-79599DiVA: diva2:543907
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Funder
Swedish Research Council
Available from: 2012-08-27 Created: 2012-08-10 Last updated: 2017-12-07Bibliographically approved

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Saha, SaikatGustafsson, Fredrik

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  • apa
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