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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.
2005 (English)In: Proceedings of the 16th IFAC World Congress, 2005, 45-45 p.Conference paper, Published paper (Refereed)
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 Cramér-Rao lower bound. Results are shown for a class of standard distributions and models in practice.

Place, publisher, year, edition, pages
2005. 45-45 p.
Keyword [en]
Kalman filters, Linear filters, Cramér-Rao lower bound, Nonlinear filters, Optimal filtering
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-30867DOI: 10.3182/20050703-6-CZ-1902.00046Local ID: 16527ISBN: 978-3-902661-75-3 (print)OAI: oai:DiVA.org:liu-30867DiVA: diva2:251690
Conference
16th IFAC World Congress, Prague, Czech Republic, July, 2005
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2015-09-22

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Hendeby, GustafGustafsson, Fredrik

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  • nn-NB
  • sv-SE
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  • asciidoc
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