Subband adaptive filtering using a multiple-constraint optimization criterion

In this paper we propose a new design criterion for subband adaptive filters (SAFs). The proposed multiple-constraint optimization criterion is based on the principle of minimal disturbance, where the multiple constraints are imposed on the updated subband filter outputs. Compared to the classical fullband least-mean-square (LMS) algorithm, the subband adaptive filtering algorithm derived from the proposed criterion exhibits faster convergence under colored excitation. Furthermore, the recursive tap-weight adaptation can be expressed in a simple form comparable to that of the normalized LMS (NLMS) algorithm. We also show that the proposed criterion is related to another known weighted criterion. The efficacy of the proposed criterion and algorithm are examined and validated via mathematical analysis and simulation.