Enhanced Normalized Subband Adaptive Filter with Variable Step Size

In this paper, we propose a variable step size algorithm to enhance the normalized subband adaptive filter which has been proposed to improve the convergence characteristics of the conventional full band adaptive filter. The well-known Kwong's variable step size algorithm is simple, but shows better performance than that of the fixed step size algorithm. However, in case that large additive noise is present, the performance of Kwong's algorithm is getting deteriorated in proportion to the amount of the additive noise. We devised a variable step size algorithm which does not depend on the amount of additive noise by exploiting a normalized adaptation error which is the error subtracted and normalized by the estimated additive noise. We carried out a performance comparison of the proposed algorithm with other algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments.