Title :
Performance analysis of general tracking algorithms
Author :
Guo, Lei ; Ljung, Lennart
Author_Institution :
Inst. of Syst. Sci., Acad. Sinica, Beijing, China
Abstract :
A general family of tracking algorithms for linear regression models is studied. It includes the familiar LMS (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. Moreover, the approximation error can be explicitly calculated
Keywords :
Kalman filters; covariance matrices; identification; least squares approximations; tracking; Kalman filter; covariance matrix; gradient approach; linear regression models; parameter tracking algorithms; recursive least squares; tracking error; Adaptive algorithm; Approximation error; Councils; Covariance matrix; Least squares approximation; Linear regression; Performance analysis; Recursive estimation; Resonance light scattering; Vectors;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411366
