Comments on "A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis"

Authors:
Neil J. Bershad
Affiliations:
Henry Samueli School of Engineering, University of California, Irvine, CA
Venue:
IEEE Transactions on Signal Processing
Year:
2009

Citing 5
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Reply to "Comments on 'A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis'"

IEEE Transactions on Signal Processing

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Abstract

A recent paper [S. C. Chan and Y. X. Zou, "A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis," IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 57, no. 1, Jaunary 2008] studied the behavior of a recursive least M-estimate (RLM) adaptive filtering algorithm in an additive impulsive noise environment. The mean and mean-square behavior of the algorithm was analyzed using a joint Gaussian assumption for the input and the error signal. This note points out that this assumption contradicts the probability model for the impulsive noise [contaminated Gaussian (CG) noise]. Hence, the analytic results presented in Chan and Zou are of limited interest.