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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)In: Proceedings of Reglermöte 2004, 2004Conference paper, Published paper (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
2004.
Keyword [en]
Parameter estimation, Linear estimation, Maximum likelihood estimators, Model approximation, Performance analysis
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-22543Local ID: 1806OAI: oai:DiVA.org:liu-22543DiVA: diva2:242856
Conference
Reglermöte 2004, Göteborg, Sweden, May, 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2015-09-22

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Hendeby, GustafGustafsson, Fredrik

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