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

• URL: http://arxiv.org/abs/2205.07172v1
• Date: Sun, 15 May 2022 03:38:13 GMT
• Title: Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization
• Authors: Yi Yu, Zongxin Huang, Hongsen He, Yuriy Zakharov and Rodrigo C. de Lamare
• Abstract summary: We propose 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.
• Score: 27.43948386608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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