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Performance Analysis of General Tracking Algorithms
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Chinese Academy of Sciences, China.
1994 (English)In: Proceedings of the 33rd IEEE Conference on Decision and Control, 1994, 2851-2855 vol.3 p.Conference paper, Published paper (Refereed)
Abstract [en]

A general family of tracking algorithms for linear regression models is studied. It includes the familiar LMS (gradient approach), RLS (recursive least squares) and KF (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 whole time interval, including the transient and the approximation error can be explicitly calculated.

Place, publisher, year, edition, pages
1994. 2851-2855 vol.3 p.
Keyword [en]
Kalman filters, Identification, Least squares approximations, Tracking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-94065DOI: 10.1109/CDC.1994.411366ISBN: 0-7803-1968-0 (print)OAI: oai:DiVA.org:liu-94065DiVA: diva2:629440
Conference
33rd IEEE Conference on Decision and Control, Lake Buena Vista, FL, USA, December, 1994
Available from: 2013-06-17 Created: 2013-06-16 Last updated: 2013-06-17

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Ljung, Lennart

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