liu.seSearch for publications in DiVA
Change search
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
Risk-Sensitive Particle Filters for Mitigating Sample Impoverishment
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.
2008 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, 5001-5012 p.Article in journal (Refereed) Published
Abstract [en]

Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error (MSE). The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. It is first shown that RSF can be done in nonlinear systems using a recursion of an infinite dimensional information state which involves general risk functions. Then, this information state calculation is carried out using particle approximations. Some alternative approaches, generalizations, specific cases, comparison to existing methods of sample impoverishment mitigation and issues related to the selection of risk functions and parameters are examined. Performance of the resulting filter using various risk functions is illustrated on a simulated scenario and compared with the roughening method.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2008. Vol. 56, no 10 II, 5001-5012 p.
Keyword [en]
Depletion, Particle filter, Risk sensitive, Sample degeneracy, Sample impoverishment
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-49765DOI: 10.1109/TSP.2008.928520OAI: oai:DiVA.org:liu-49765DiVA: diva2:270661
Note

© 2008 IEEE.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-07-22

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Orguner, UmutGustafsson, Fredrik

Search in DiVA

By author/editor
Orguner, UmutGustafsson, Fredrik
By organisation
Automatic ControlThe Institute of Technology
In the same journal
IEEE Transactions on Signal Processing
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 165 hits
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