Inference in Mixed Linear/Nonlinear State-Space Models using Sequential Monte Carlo
2010 (English)Report (Other academic)
In this work we apply sequential Monte Carlo methods, i.e., particle filters and smoothers, to estimate the state in a certain class of mixed linear/nonlinear state-space models. Such a model has an inherent conditionally linear Gaussian substructure. By utilizing this structure we are able to address even high-dimensional nonlinear systems using Monte Carlo methods, as long as only a few of the states enter nonlinearly. First, we consider the filtering problem and give a self-constained derivation of the well known Rao-Blackellized particle filter. Therafter we turn to the smoothing problem and derive a Rao-Blackwellized particle smoother capable of handling the fully interconnected model under study.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 31 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2946
SMC- -Particle filter--Particle smoother--Rao-Blackwellization
IdentifiersURN: urn:nbn:se:liu:diva-97603ISRN: LiTH-ISY-R-2946OAI: oai:DiVA.org:liu-97603DiVA: diva2:649236