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. 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.

Automated summary

This paper proposes a unified sparsity-aware robust recursive least-squares (S-RRLS) algorithm for identifying sparse systems under impulsive noise. By replacing the specified criterion of robustness and sparsity-aware penalty, the proposed algorithm generalizes multiple algorithms. Furthermore, the jointly-optimized S-RRLS (JO-S-RRLS) algorithm is developed by optimizing the forgetting factor and the sparsity penalty parameter, which exhibits low misadjustment and tracks sudden changes effectively. Simulation results in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.