DocumentCode :
932935
Title :
A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis
Author :
Chan, Shing-Chow ; Zou, Yue-Xian
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, China
Volume :
52
Issue :
4
fYear :
2004
fDate :
4/1/2004 12:00:00 AM
Firstpage :
975
Lastpage :
991
Abstract :
This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.
Keywords :
Gaussian distribution; Gaussian noise; adaptive filters; convergence of numerical methods; feedback; impulse noise; lattice filters; mean square error methods; Gaussian impulsive noise; Huber prior error feedback-least squares lattice algorithm; adaptive filter; arithmetic complexity; contaminated Gaussian distribution; convergence; cost function; mean square error function; recursive least M-estimate algorithm; robust adaptive filtering; robust statistics; system identification; Adaptive filters; Convergence; Cost function; Filtering algorithms; Lattices; Mean square error methods; Noise robustness; Performance analysis; Resonance light scattering; Working environment noise;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2004.823496
Filename :
1275671
Link To Document :
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