Abstract To ameliorate the performance of the normalized least mean square (NLMS) algorithm applied in identifying block-sparse system, a novel block-sparse proportionate NLMS (BS-PNLMS) algorithm based on the mixed l2,0 norm is presented in this paper. The cost function of proposed algorithm is obtained by introducing the approximated mixed l2,0 norm penalty in the cost function of BS-PNLMS algorithm, which is equivalent to attaching the mixed l2,0 norm zero attraction to the update equation. The proposed algorithm is named as the l2,0-BS-PNLMS algorithm. Futhermore, a variable step-size l2,0-BS-PNLMS is developed to further improve the performance of the l2,0-BS-PNLMS. Simulation results demonstrate the advantages of proposed algorithms.

中文翻译：

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一种基于l2,0范数的新型块稀疏比例NLMS算法

摘要 为了提高归一化最小均方(NLMS)算法在识别块稀疏系统中的性能，提出了一种基于混合l2,0范数的块稀疏比例NLMS(BS-PNLMS)算法。该算法的代价函数是在BS-PNLMS算法的代价函数中引入近似混合l2,0范数惩罚得到的，相当于在更新方程中附加了混合l2,0范数零吸引力。所提出的算法被命名为l2,0-BS-PNLMS算法。此外，还开发了可变步长 l2,0-BS-PNLMS 以进一步提高 l2,0-BS-PNLMS 的性能。仿真结果证明了所提出算法的优点。