Wavelet transform domain LMS algorithm

Author

Hosur, Srinath ; Tewfik, Ahmed H.

Author_Institution

Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA

Volume

3

fYear

1993

fDate

27-30 April 1993

Firstpage

508

Abstract

A novel normalized wavelet domain least-mean-square (LMS) algorithm is described. The faster convergence rate of this algorithm as compared with time-domain LMS is established. The wavelet domain LMS algorithm requires only real arithmetic. In its most basic form it has a computational complexity that is higher than that of the traditional LMS technique by a factor of cN, where N is the length of the transformed vector (or sliding analysis window) and c is the length of the analysis wavelet. Other preconditioning strategies that yield a faster convergence rate for a given fixed excess mean squared error are discussed. The authors also describe low-complexity implementations of the wavelet domain LMS algorithm. These implementations exploit the structure of the wavelet transform of the underlying stochastic process.<>

Keywords

adaptive filters; computational complexity; convergence of numerical methods; digital arithmetic; filtering and prediction theory; least squares approximations; wavelet transforms; LMS algorithm; adaptive filtering; computational complexity; convergence rate; preconditioning strategies; real arithmetic; stochastic process; wavelet domain;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on

Conference_Location

Minneapolis, MN, USA

ISSN

1520-6149

Print_ISBN

0-7803-7402-9

Type

conf

DOI

10.1109/ICASSP.1993.319546

Filename

319546