A Variable Regularization Matrix Normalized Subband Adaptive Filter

The normalized subband adaptive filter (NSAF) proposed by Lee and Gan is promising. However, there exists the conflicting requirement of fast convergence rate and low misadjustment for the NSAF. In this letter, we propose a variable regularization matrix NSAF (VRM-NSAF) to address this problem. The optimal selection of the regularization matrix is derived by the largest decrease of the mean-square deviation (MSD). Simulation results comparing the proposed VRM-NSAF with the original NSAF are presented to show the advantage of this method, including both fast convergence rate and low misadjustment.