Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization

TL;DR

This paper introduces a unified sparsity-aware robust normalized subband adaptive filtering algorithm that adapts parameters via alternating optimization, achieving fast convergence and low steady-state error in sparse system identification under impulsive noise.

Contribution

It develops the AOP-SA-RNSAF algorithm with parameter optimization, improving convergence speed and accuracy over existing methods for sparse systems.

Findings

Outperforms existing techniques in various noise scenarios

Achieves fast convergence and low steady-state misadjustment

Effectively handles impulsive noise in sparse system identification

Abstract

This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steady-state misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques.