Particle Filtering With Dependent Noise Processes
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
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.
Bayesian methods, Dependent noise, Particle ﬁlters, Rao–Blackwellized particle ﬁlter, Recursive estimation
Signal Processing Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-79599DOI: 10.1109/TSP.2012.2202653ISI: 000307790800001OAI: oai:DiVA.org:liu-79599DiVA: diva2:543907
FunderSwedish Research Council