Performance analysis of general tracking algorithms

Author

Guo, Lei ; Ljung, Lennart

Author_Institution

Inst. of Syst. Sci., Acad. Sinica, Beijing, China

Volume

40

Issue

8

fYear

1995

fDate

8/1/1995 12:00:00 AM

Firstpage

1388

Lastpage

1402

Abstract

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

Keywords

Kalman filters; adaptive control; adaptive signal processing; covariance matrices; least mean squares methods; parameter estimation; tracking; Kalman filter; adaptive algorithm; approximation error; covariance matrix; least mean square gradient method; linear regression models; parameter tracking error; performance analysis; tracking algorithms; transient; Adaptive algorithm; Approximation error; Covariance matrix; Filters; History; Least squares approximation; Linear regression; Performance analysis; Recursive estimation; Vectors;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.402230

Filename

402230