On Error Saturation Nonlinearities for LMS Adaptation in Impulsive Noise

Author

Bershad, Neil J.

Author_Institution

Univ. of California, Newport Beach, CA

Volume

56

Issue

9

fYear

2008

Firstpage

4526

Lastpage

4530

Abstract

This correspondence extends the analytic results in [N. J. Bershad, ldquoOn error nonlinearities in LMS adaptation,rdquo IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-36, no. 4, pp. 440-452, April 1988] to least mean-square (LMS) adaptation in impulsive observation noise. A scalar recursion for the weight misadjustment is derived for the white input data case. Monte Carlo simulations verify the accuracy of the theoretical model. The theoretical recursion is then used to study the effects of the impulse noise on algorithm convergence speed and steady-state weight misadjustment for a wide variety of parameter values.

Keywords

Monte Carlo methods; adaptive signal processing; convergence of numerical methods; impulse noise; least mean squares methods; nonlinear filters; LMS adaptation; Monte Carlo simulations; algorithm convergence speed; error saturation nonlinearities; impulsive noise; impulsive observation noise; least mean-square adaptation; scalar recursion; steady-state weight misadjustment; weight misadjustment; Adaptive filters; adaptive signal processing; adaptive systems; nonlinear filters;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2008.926103

Filename

4531182