Performance Analysis of General Tracking Algorithms
1994 (English)In: Proceedings of the 33rd IEEE Conference on Decision and Control, 1994, 2851-2855 vol.3 p.Conference paper (Refereed)
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.
Kalman filters, Identification, Least squares approximations, Tracking
IdentifiersURN: urn:nbn:se:liu:diva-94065DOI: 10.1109/CDC.1994.411366ISBN: 0-7803-1968-0OAI: oai:DiVA.org:liu-94065DiVA: diva2:629440
33rd IEEE Conference on Decision and Control, Lake Buena Vista, FL, USA, December, 1994