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Risk Sensitive Particle Filters for Mitigating Sample Impoverishment
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2007 (English)In: Proceedings of the 14th IEEE/SP Workshop on Statistical Signal Processing, 2007, , 259-263 p.259-263 p.Conference paper, Published paper (Refereed)
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. 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.

Place, publisher, year, edition, pages
2007. , 259-263 p.259-263 p.
Keyword [en]
Risk sensitive, Particle filter
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-42378DOI: 10.1109/SSP.2007.4301259Local ID: 63439ISBN: 978-1-4244-1198-6 (print)OAI: oai:DiVA.org:liu-42378DiVA: diva2:263234
Conference
14th IEEE/SP Workshop on Statistical Signal Processing, Madison, WI, USA, August, 2007
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-23

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Orguner, UmutGustafsson, Fredrik

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  • nn-NB
  • sv-SE
  • Other locale
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