Variable regularization for normalized subband adaptive filter

To overcome the performance degradation of least mean square (LMS)-type algorithms when input signals are correlated, the normalized subband adaptive filter (NSAF) was developed. In the NSAF, the regularization parameter influences the stability and performance. In addition, there is a trade-off between convergence rate and steady-state mean square deviation (MSD) according to the change of the parameter. Therefore, to achieve both fast convergence rate and low steady-state MSD, the parameter should be varied. In this paper, a variable regularization scheme for the NSAF is derived on the basis of the orthogonality between the weight-error vector and weight vector update, and by using the calculated MSD. The performance of the variable regularization algorithm is evaluated in terms of MSD. Our simulation results exhibit fast convergence and low steady-state MSD when using the proposed algorithm.