论文标题
对冲动噪声环境共同优化参数的鲁棒性稀疏性RLS算法的研究
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments
论文作者
论文摘要
本文提出了一种统一的稀疏性稳健递归最小二乘RLS（S-RRLS）算法，用于鉴定冲动噪声下稀疏系统。拟议的算法仅通过替换鲁棒性和稀疏感惩罚的指定标准来概括多种算法。此外，通过共同优化遗忘因子和稀疏性惩罚参数，我们开发了共同优化的S-RRL（JO-S-RRLS）算法，该算法不仅表现出较低的错误调整，而且可以跟踪稀疏系统的突然变化。冲动噪声场景中的模拟表明，所提出的S-RRL和JO-S-RRLS算法的表现优于现有技术。
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.
