Quantifying the effects of dimension on the convergence rate of the LMS adaptive FIR estimator

Author

Homer, John ; Bitmead, Robert R. ; Mareels, Iven

Author_Institution

Dept. of Comput. Sci. & Electr. Eng., Queensland Univ., Brisbane, Qld., Australia

Volume

46

Issue

10

fYear

1998

fDate

10/1/1998 12:00:00 AM

Firstpage

2611

Lastpage

2615

Abstract

The convergence rate of an LMS adaptive FIR filter to an unknown stationary channel may be influenced by the filter parameter dimension as well as by the input signal´s characteristics. This dimension influence may be of importance in applications, such as adaptive acoustic echo cancellation, in which the unknown channel is typically modeled as a “long” FIR filter. The paper includes the development and proposal of a novel measure of the expected convergence rate of the LMS/FIR filter followed by analysis of this convergence rate measure. The analysis indicates that unless the input signal is white, the expected convergence rate decreases with increasing dimension down to a limiting value, which is determined by the input signal´s autocorrelation level

Keywords

FIR filters; adaptive estimation; adaptive filters; adaptive signal processing; convergence of numerical methods; correlation methods; echo suppression; least mean squares methods; LMS adaptive FIR estimator; LMS/FIR filter; adaptive FIR filter; adaptive acoustic echo cancellation; autocorrelation level; convergence rate; filter parameter dimension; input signal characteristics; long FIR filter; stationary channel; white input signal; Acoustic applications; Acoustic measurements; Adaptive filters; Autocorrelation; Convergence; Echo cancellers; Finite impulse response filter; Least squares approximation; Proposals; Signal analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.720364

Filename

720364