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

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....

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Bibliographic Details
Published in:IEEE signal processing letters 2022, Vol.29, p.1037-1041
Main Authors: Yu, Yi, Lu, Lu, Zakharov, Yuriy, Lamare, Rodrigo C. de, Chen, Badong
Format: Article
Language:English
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Summary: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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3166395