Robust Recursive Least-Squares Adaptive-Filtering Algorithm for Impulsive-Noise Environments

Author

Bhotto, Md Zulfiquar Ali ; Antoniou, Andreas

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, Canada

Volume

18

Issue

3

fYear

2011

fDate

3/1/2011 12:00:00 AM

Firstpage

185

Lastpage

188

Abstract

A new robust recursive least-squares (RLS) adaptive filtering algorithm that uses a priori error-dependent weights is proposed. Robustness against impulsive noise is achieved by choosing the weights on the basis of the L₁ norms of the crosscorrelation vector and the input-signal autocorrelation matrix. The proposed algorithm also uses a variable forgetting factor that leads to fast tracking. Simulation results show that the proposed algorithm offers improved robustness as well as better tracking compared to the conventional RLS and recursive least-M estimate adaptation algorithms.

Keywords

adaptive filters; correlation methods; impulse noise; least squares approximations; matrix algebra; recursive estimation; recursive filters; signal denoising; a priori error-dependent weights; crosscorrelation vector; impulsive-noise environments; input-signal autocorrelation matrix; recursive least-M estimate adaptation algorithms; robust recursive least-squares adaptive-filtering algorithm; variable forgetting factor; Convergence; Equations; Mathematical model; Noise; Robustness; Simulation; Steady-state; Adaptive filters; RLS adaptation algorithms; robust adaptation algorithms;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2011.2106119

Filename

5688223