Work

Robust Sparsity-Aware RLS Algorithms With Jointly-Optimized Parameters Against Impulsive Noise

Year: 2022

Type: article

Abstract: This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustnessand sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2022.3166395

Authors: Yi Yu, Lu Lu, Yuriy Zakharov, Rodrigo C. de Lamare, Badong Chen

Institutions: Southwest University of Science and Technology, Sichuan University, University of York, Pontifícia Universidade Católica do Rio de Janeiro, Xi'an Jiaotong University

Language: English

Topic: Advanced Adaptive Filtering Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green