Convergence analysis of the recursive least M-estimate adaptive filtering algorithm for impulse noise suppression

Author

Chan, Shing-Chow ; Zou, Yuc-Xian

Author_Institution

Dept. of Elecctrical & Electron. Eng., Univ. of Hong Kong, China

Volume

2

fYear

2002

fDate

2002

Firstpage

663

Abstract

We present the convergence analysis of the recursive least M-estimate (RLM) adaptive filter algorithm, which was recently proposed for robust adaptive filtering in the impulse noise environment. The mean and mean squares behaviors of the RLM algorithm, based on the modified Huber M-estimate function (MHF), in the contaminated Gaussian (CG) noise model are analyzed. Close-form expressions are derived. The simulation and theoretical results agree very well with each other and suggest that the RLM algorithm is more robust than the RLS algorithm under the CG noise model.

Keywords

Gaussian noise; adaptive filters; convergence of numerical methods; filtering theory; impulse noise; interference suppression; recursive estimation; recursive filters; adaptive filtering; contaminated Gaussian noise; convergence analysis; impulse noise suppression; modified Huber M-estimate function; recursive least M-estimate; Adaptive filters; Algorithm design and analysis; Character generation; Computational modeling; Convergence; Cost function; Filtering algorithms; Gaussian noise; Noise robustness; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on

Print_ISBN

0-7803-7503-3

Type

conf

DOI

10.1109/ICDSP.2002.1028178

Filename

1028178