General Robust Proportionate NSAF Algorithm With a Step-Size Converter

Abstract

To identify sparse systems in the presence of impulsive noises, we propose a general robust proportionate normalized subband adaptive filtering (R-PNSAF) algorithm. Furthermore, to achieve fast convergence and low steady-state misadjustment, we develop a step-size converter (SSC) for R-PNSAF which results in the SSC-R-PNSAF algorithm, which selects the optimal step-size by comparing the mean square deviation at each iteration of the algorithm under given different step-sizes. Simulation results demonstrate the superiority of the proposed scheme in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -stable noise scenario over the competing techniques.