DocumentCode :
1391335
Title :
A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulse noise
Author :
Zou, Y. ; Chan, S.C. ; Ng, T.S.
Author_Institution :
Dept. of Electr. & Electron. Eng., Hong Kong Univ., China
Volume :
7
Issue :
11
fYear :
2000
Firstpage :
324
Lastpage :
326
Abstract :
This paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and the OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.
Keywords :
Gaussian noise; adaptive filters; computational complexity; convergence of numerical methods; filtering theory; impulse noise; recursive estimation; recursive filters; Gaussian noise; M-estimate cost function; RLM algorithm; RLS algorithm; adaptive filter; computational complexity; convergence; impulse noise; noise suppression; recursive least M-estimate algorithm; robust filtering; simulation results; steady-state error; Adaptive filters; Computational complexity; Computational modeling; Convergence; Cost function; Filtering algorithms; Gaussian noise; Noise robustness; Resonance light scattering; Steady-state;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/97.873571
Filename :
873571
Link To Document :
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