论文标题
基于交替优化
Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization
论文作者
论文摘要
本文提出了一种统一的稀疏性稀疏性鲁棒标准化子带自适应滤波（SA-RNSAF）算法，用于在冲动噪声下识别稀疏系统。拟议的SA-RNSAF算法通过定义强大的标准和稀疏感知的惩罚来概括不同的算法。此外，通过优化算法的参数（AOP），包括阶梯尺寸和稀疏性惩罚权重，我们开发了AOP-SA-RNSAF算法，该算法不仅表现出快速收敛性，而且还会表现出对稀疏系统的低稳态不足。在各种噪声方案中的模拟已经验证了所提出的AOP-SA-RNSAF算法优于现有技术。
This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steady-state misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques.
