P-Norm Based Subband Adaptive Filtering Algorithm

The normalized subband adaptive filtering algorithm can improve the convergence rate of the normalized least mean square algorithm when dealing with the correlated input signals, but it is plagued by a slow convergence issue in the stable noise. For that reason, the normalized subband p-norm (NSPN) algorithm based on the mean p-power error criterion is proposed in this paper, which shows a fast convergence rate in the α-stable noise. Moreover, by taking advantage of the tap-weights feedback-based convex combination (TFC) scheme, we propose the TFC based NSPN algorithm, which further reaches low steady-state misadjustment under fast convergence. Simulation results have confirmed the superior performance of the proposed algorithms in both system identification and acoustic echo cancellation scenarios.