Quantifying the convergence speed of LMS adaptive FIR filter with autoregressive inputs

Author

Homer, J.

Author_Institution

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

Volume

36

Issue

6

fYear

2000

fDate

3/16/2000 12:00:00 AM

Firstpage

585

Lastpage

586

Abstract

In general the least mean squares adaptive finite impulse response (FIR) filter converges more slowly with an increase in filter length and input signal correlation level. An explicit expression is presented relating the convergence speed of this adaptive filter to the FIR filter length and the correlation characteristics of autoregressive (AR) modelled input signals. The expression provides a simple means for justifying (or not) the cost of input signal whitening techniques within for example acoustic echo cancellation, in which very large FIR filter lengths and highly correlated AR modelled speech input signals occur

Keywords

FIR filters; adaptive filters; autoregressive processes; convergence of numerical methods; correlation theory; digital filters; echo suppression; filtering theory; least mean squares methods; AR modelled input signals; LMS adaptive FIR filter; acoustic echo cancellation; autoregressive inputs; convergence speed; correlation characteristics; filter length; finite impulse response filter; input signal correlation level; input signal whitening techniques; least mean squares type;

fLanguage

English

Journal_Title

Electronics Letters

Publisher

iet

ISSN

0013-5194

Type

jour

DOI

10.1049/el:20000469

Filename

840184