An Improved Multiband-Structured Subband Adaptive Filter Algorithm

Abstract

Recently, a multiband-structured subband adaptive filter (MSAF) algorithm was proposed to speed up the convergence of the normalized least-mean-square (NLMS) algorithm. In this letter, we extend this work and propose an improved multiband-structured subband adaptive filter (IMSAF) algorithm to increase the convergence speed of the MSAF, which can also be regarded as a unifying framework for the NLMS, MSAF, and affine projection (AP) algorithms. The proposed optimization criterion is based on the principle of minimal disturbance, canceling the most recent P a posteriori errors in each of the N subbands. The stability condition and the computational complexity are also analyzed. Computer simulations in the context of system identification demonstrate the effectiveness of the new algorithm.