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Fundamental Filtering Limitations in Linear Non-Gaussian Systems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2004 (English)Report (Other academic)
Abstract [en]

The Kalman filter is known to be the optimal linear filter for linear non-Gaussian systems. However, nonlinear filters such as Kalman filter banks and more recent numerical methods such as the particle filter are sometimes superior in performance. Here a procedure to a priori decide how much can be gained using nonlinear filters, without having to resort to Monte Carlo simulations, is outlined. The procedure is derived in terms of the posterior Cramer-Rao lower bound. Results are shown for a class of standard distributions and models in practice.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2640
Keyword [en]
Kalman filters, Linear filters, Cramer-rao lower bound, Nonlinear filters, Optimal filtering
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56005ISRN: LiTH-ISY-R-2640OAI: oai:DiVA.org:liu-56005DiVA: diva2:316727
Funder
Vinnova
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2015-09-22Bibliographically approved

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Hendeby, GustafGustafsson, Fredrik

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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