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Performance Analysis of General Tracking Algorithms
Chinese Academy of Sciences, China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1995 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 40, no 8, 1388-1402 p.Article in journal (Refereed) Published
Abstract [en]

A general family of tracking algorithms for linear regression models is studied. It includes the familiar least mean square gradient approach, recursive least squares, and Kalman filter based estimators. The exact expressions for the quality of the obtained estimates are complicated. Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed. These are applicable over the whole time interval, including the transient, and the approximation error can be explicitly calculated.

Place, publisher, year, edition, pages
1995. Vol. 40, no 8, 1388-1402 p.
Keyword [en]
Kalman filters, Adaptive control, Adaptive signal processing, Covariance matrices, Parameter estimation, Tracking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56343DOI: 10.1109/9.402230OAI: oai:DiVA.org:liu-56343DiVA: diva2:318616
Funder
Swedish Research Council
Available from: 2010-05-09 Created: 2010-05-07 Last updated: 2017-12-12

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Ljung, Lennart

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