Adaptive regularisation for normalised subband adaptive filter: mean-square performance analysis approach

Abstract

The normalised subband adaptive filter ( NSAF) is a useful adaptive filter, which improves the convergence rate compared with the normalised least mean- square algorithm. Most analytical results of the NSAF set the regularisation parameter set to zero or present only steady- state mean- square error performance of the regularised NSAF ( e- NSAF). This study presents a mean- square performance analysis of e- NSAF, which analyses not only convergence behaviour but also steady- state behaviour. Furthermore, a novel adaptive regularisation for NSAF ( AR- NSAF) is also developed based on the proposed analysis approach. The proposed AR- NSAF selects the optimal regularisation parameter that leads to improving the performance of the adaptive filter. Simulation results comparing the proposed analytical results with the results achieved from the simulation are presented. In addition, these results verify that the proposed AR- NSAF outperforms the previous algorithms in a system- identification and acoustic echo- cancellation scenarios.