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This paper studies an M-estimation-based method for linear estimation with weighted L1 regularization and its recursive implementation. Motivated by the sensitivity of conventional least-squares-based L1-regularized linear estimation (Lasso) in impulsive noise environment, an M-estimator-based Lasso (M-Lasso) method is introduced to restrain the outliers and an iterative re-weighted least-squares...
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:...
This paper proposes a new noise-constrained normalized least mean squares (NC-NLMS) adaptive filtering algorithm and studies its mean and mean square convergence behaviors. The new NC-NLMS algorithm is obtained by extending the noise-constrained LMS (NC-LMS) algorithm of Wei, which was proposed to explore the prior information on the noise variance in identifying unknown finite impulse response channels...
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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