Abstract
This letter proposes a set-membership normalised least M-estimate algorithm based on Wiener spline adaptive filter (SAF). The proposed algorithm combines the set-membership framework and least-M estimate scheme, thus achieving faster convergence rate and effective suppression of impulsive noise on the filter weight and control point adaptation. Simulation results demonstrate that the proposed one exhibits more robust performance compared to the conventional SAF algorithms in an impulsive noise environment.
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