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On Performance Measures for Approximative Parameter Estimation
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 computes the minimum variance state estimate as a linear function of measurements in the case of a linear model with Gaussian noise processes. There are plenty of examples of non-linear estimators that outperform the Kalman filter when the noise processes deviate from Gaussianity, for instance in target tracking with occasionally maneuvering targets. Here we present, in a preliminary study, a detailed analysis of the well-known parameter estimation problem. This time with Gaussian mixture measurement noise. We compute the discrepancy of the best linear unbiased estimator BLUE and the Cramer-Rao lower bound, and based on this conclude when computationally intensive Kalman filter banks or particle filters may be used to improve performance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2608
Keyword [en]
Parameter estimation, Linear estimation, Maximum likelihood estimators, Model approximation, Performance analysis
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55987ISRN: LiTH-ISY-R-2608OAI: oai:DiVA.org:liu-55987DiVA: diva2:316749
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

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