Tracking of linear time-varying systems using state-space least mean square

Author

Malik, Mohammad Bilal ; Bhatti, Rashid Ahmad

Author_Institution

Coll. of Electr. & Mech. Eng., Nat. Univ. of Sci. & Technol., Pakistan

Volume

1

fYear

2004

fDate

26-29 Oct. 2004

Firstpage

582

Abstract

In this paper, we present a generalized least mean square (LMS) algorithm. This new filter, which has been termed as state-space least mean square (SSLMS), incorporates linear time-varying state-space model of the underlying environment. The tracking ability of the LMS is limited due to linear regression model assumption. By overcoming this restriction, SSLMS exhibits a marked improvement in tracking performance over standard LMS and its known variants. The derivation of SSLMS is based on the minimum norm solution of an underdetermined linear least squares problem. An example of tracking a linear time-varying system demonstrates the ability and flexibility of SSLMS.

Keywords

adaptive filters; adaptive signal processing; least mean squares methods; linear systems; state-space methods; time-varying systems; tracking filters; LMS tracking ability; SSLMS filter; adaptive filtering; generalized least mean square algorithm; linear regression model assumption; linear time-varying state-space model; linear time-varying system; linear time-varying systems tracking; minimum norm solution; state-space least mean square filter; tracking performance; underdetermined linear least squares problem; underlying environment; Adaptive filters; Educational institutions; Least squares approximation; Least squares methods; Linear regression; Mechanical engineering; Nonlinear filters; Resonance light scattering; State estimation; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology, 2004. ISCIT 2004. IEEE International Symposium on

Print_ISBN

0-7803-8593-4

Type

conf

DOI

10.1109/ISCIT.2004.1412912

Filename

1412912