Best input for optimal tracking randomly time-varying systems: justification of adaptive predictive structure

Author

Jebara, S.B. ; Jaidane-Saidane, M.

Author_Institution

L.S. Telecoms, ENIT, Tunis, Tunisia

Volume

3

fYear

1997

fDate

21-24 Apr 1997

Firstpage

1957

Abstract

This paper presents a tracking analysis of the LMS algorithm used in order to identify system variations modeled by a random walk. We prove that the steady state properties are strongly related to the input characteristics. The input correlation degrades the performances. Consequently, best performances are obtained for white input. We justify the coupled adaptive predictive structures with system identification in order to improve classical scheme steady state performances

Keywords

adaptive filters; adaptive signal processing; correlation methods; filtering theory; identification; least mean squares methods; prediction theory; random processes; time-varying systems; tracking filters; LMS algorithm; adaptive predictive structure; input correlation; optimal tracking; random walk; randomly time-varying systems; steady state properties; system identification; white input; Adaptive systems; Additive noise; Algorithm design and analysis; Covariance matrix; Least squares approximation; Mean square error methods; Performance analysis; Predictive models; Steady-state; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.598926

Filename

598926