Adaptive tracking of linear time-variant systems by extended RLSalgorithms

Authors:
S. Haykin;A.H. Sayed;J.R. Zeidler;P. Yee;P.C. Wei
Affiliations:
Commun. Res. Lab., McMaster Univ., Hamilton, Ont.;-;-;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
1997

Citing 0
Cited 17

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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