Variable individual step-size subband adaptive filtering algorithm

Abstract

A subband adaptive filtering algorithm is proposed which improves its performance by adjusting step sizes. The proposed algorithm derives the individual step sizes for each subband instead of using a common step size for multiple subbands. The derivation of the step sizes is based on the mean-square deviation minimisation in order to achieve the fastest convergence at the instant. Furthermore, the individual step sizes contain the squared norm of the input vector, hence it leads to the regularisation effect that helps the algorithm work well in the case of badly excited input signals. The simulation results show that the proposed algorithm achieves a faster convergence rate and a smaller steady-state estimation error than the existing algorithms.