Adaptive tracking of linear time-variant systems by extended RLS algorithms

Author

Haykin, Simon ; Sayed, Ali H. ; Zeidler, James R. ; Yee, Paul ; Wei, Paul C.

Author_Institution

Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada

Volume

45

Issue

5

fYear

1997

fDate

5/1/1997 12:00:00 AM

Firstpage

1118

Lastpage

1128

Abstract

We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms

Keywords

Kalman filters; adaptive filters; adaptive signal processing; filtering theory; identification; least squares approximations; linear systems; noise; recursive estimation; time-varying systems; tracking filters; Kalman filter; Kalman variables; LMS algorithm; adaptive tracking; additive noise; chirped sinusoid tracking; experiments; extended RLS algorithms; least mean squares algorithm; linear time-variant systems; linear tracking device; recursive least-squares; standard RLS algorithm; system identification; Adaptive filters; Additive noise; Chirp; Convergence; Filtering algorithms; Helium; Kalman filters; Least squares approximation; Resonance light scattering; System identification;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.575687

Filename

575687