A new sequential block partial update normalized least mean M-estimate algorithm and its convergence performance analysis

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. © 2009 Springer Science+Business Media, LLC.