On Performance Measures for Approximative Parameter Estimation
2004 (English)In: Proceedings of Reglermöte 2004, 2004Conference paper (Other academic)
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
Parameter estimation, Linear estimation, Maximum likelihood estimators, Model approximation, Performance analysis
Engineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-22543Local ID: 1806OAI: oai:DiVA.org:liu-22543DiVA: diva2:242856
Reglermöte 2004, Göteborg, Sweden, May, 2004