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Inference in Mixed Linear/Nonlinear State-Space Models using Sequential Monte Carlo
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.
2010 (English)Report (Other academic)
Abstract [en]

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.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2946
Keyword [en]
SMC- -Particle filter--Particle smoother--Rao-Blackwellization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97603ISRN: LiTH-ISY-R-2946OAI: oai:DiVA.org:liu-97603DiVA: diva2:649236
Available from: 2013-09-17 Created: 2013-09-17 Last updated: 2014-09-01Bibliographically approved

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Lindsten, FredrikSchön, Thomas

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf