A New Sequential Block Partial Update Normalized Least Mean M-Estimate Algorithm and its Convergence Performance Analysis |
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Authors: | Shing Chow Chan Yi Zhou Ka Leung Ho |
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Affiliation: | (1) Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong |
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Abstract: | This paper proposes a new sequential block partial update normalized least mean square (SBP-NLMS) algorithm and its nonlinear
extension, the SBP-normalized least mean M-estimate (SBP–NLMM) algorithm, for adaptive filtering. These algorithms both utilize
the sequential partial update strategy as in the sequential least mean square (S–LMS) algorithm to reduce the computational
complexity. Particularly, the SBP–NLMM algorithm minimizes the M-estimate function for improved robustness to impulsive outliers
over the SBP–NLMS algorithm. The convergence behaviors of these two algorithms under Gaussian inputs and Gaussian and contaminated
Gaussian (CG) noises are analyzed and new analytical expressions describing the mean and mean square convergence behaviors
are derived. The robustness of the proposed SBP–NLMM algorithm to impulsive noise and the accuracy of the performance analysis
are verified by computer simulations. |
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