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This paper studies the convergence behaviors of the fast least mean M-estimate/Newton adaptive filtering algorithm proposed in (Y. Zhou et al.,2004), which is based on the fast LMS/Newton principle and the minimization of an M-estimate function using robust statistics for robust filtering in impulsive noise. By using the Price's theorem and its extension for contaminated Gaussian (CG) noise case,...
We appreciate the comments by Bershad [ldquoComments on `A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis,'rdquo IEEE Transactions on Signal Processing, vol. 57, no. 1, January 2009] on an assumption of our paper [ldquoA Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise:...
In this paper, we study the convergence performance of the transform domain normalized least mean square (TDNLMS) algorithm and its robust version, the TD normalized least mean M-estimate (TDNLMM) algorithm, which is derived from robust M-estimation and has the improved performance over their conventional TDNLMS counterpart in impulsive noise environment. Using the Pricepsilas theorem and its extension,...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the normalized least mean M-estimate (NLMM) algorithms. Our analysis is obtained by extending the framework of Bershad [6], [7], which were previously reported for the NLMS algorithm with Gaussian inputs. Due to the difficulties in evaluating certain expectations involved, in [6], [7] the behaviors of the NLMS...
This paper proposes a new sequential block partial update normalized least mean M-estimate (SB-NLMM) algorithm for adaptive filtering in impulsive noise environment. It utilizes the sequential partial update concept as in the sequential block partial update normalized least mean square (SB-NLMS) algorithm to reduce the computational complexity, while minimizing the M-estimate function for improved...
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