Adaptive filters with nonlinear RLS algorithm in impulse noise

Author

Leung, Shu-hung ; Weng, Jian-feng

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Hong Kong

Volume

3

fYear

1999

fDate

36342

Firstpage

37

Abstract

Adaptive filter using the nonlinear recursive least square (RLS) algorithm in impulse noise is presented. Its mean and mean square behaviors are studied for a Gaussian input signal and the class A impulse noise. Necessary and sufficient conditions for the stability of the algorithm are derived. A class of nonlinear functions including the linear one, which can be incorporated into RLS for assuring stable learning, is introduced. By simulations it is shown that the nonlinear algorithm not only provides convergence rate as fast as but also has excess mean squared error smaller than the linear counterpart in impulse noise

Keywords

adaptive filters; convergence of numerical methods; filtering theory; impulse noise; least squares approximations; nonlinear functions; stability; Gaussian input signal; adaptive filters; class A noise; convergence rate; impulse noise; mean behavior; mean square behavior; nonlinear RLS algorithm; nonlinear functions; recursive least square algorithm; stability; stable learning; Adaptive filters; Convergence; Finite impulse response filter; Gaussian noise; Least squares approximation; Noise robustness; Recursive estimation; Resonance light scattering; Vectors; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-5471-0

Type

conf

DOI

10.1109/ISCAS.1999.778779

Filename

778779