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Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments
- Publication Year :
- 2022
-
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 robustness and 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.<br />Comment: 7 pages, 2 figures
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2204.08990
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/LSP.2022.3166395