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A Basic Convergence Result for Particle Filtering
Chinese Academy of Sciences, China.
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.
2007 (English)Report (Other academic)
Abstract [en]

The basic nonlinear filtering problem for dynamical systems is considered. Approximating the optimal filter estimate by particle filter methods has become perhaps the most common and useful method in recent years. Many variants of particle filters have been suggested, and there is an extensive literature on the theoretical aspects of the quality of the approximation. Still a clear cut result that the approximate solution, for unbounded functions, converges to the true optimal estimate as the number of particles tends to infinity seems to be lacking. It is the purpose of this contribution to give such a basic convergence result for a rather general class of unbounded functions. Furthermore, a general framework, including many of the particle filter algorithms as special cases, is given.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. , 13 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2824
Keyword [en]
Nonlinear filters, Particle filter, Convergence, Dynamic systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56140ISRN: LiTH-ISY-R-2824OAI: oai:DiVA.org:liu-56140DiVA: diva2:316957
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-12Bibliographically approved

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Authority records BETA

Hu, Xiao-LiSchön, ThomasLjung, Lennart

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CiteExportLink to record
Permanent link

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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