# Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments

arXiv: https://arxiv.org/abs/2204.08990 · HTML: https://mlsanity.com/paper/2204.08990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 98 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems](https://mlsanity.com/paper/1410.1390/) (2014)
- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Generalized Correntropy for Robust Adaptive Filtering](https://mlsanity.com/paper/1504.02931/) (2015)
- [Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximation](https://mlsanity.com/paper/1804.00462/) (2018)
- [Recursive Geman-McClure method for implementing second-order Volterra filter](https://mlsanity.com/paper/1808.00613/) (2018)

## Abstract (arXiv)

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.