Abstract The l0-norm-constraint LMS (l0-LMS) algorithm is one of the widely used sparse LMS algorithms for the identification of sparse system, and its performance is quite attractive compared to other precursors. However, l0-LMS is still confronted with some limitations on the optimal parameter selection and the estimated coefficient accuracy of sparse system identification. In this paper, we proposed an improved l0-LMS (l0-ILMS) algorithm to address these limitations. The convergence condition and the parameter selection rules for optimal steady-state mean-square deviation (MSD) of l0-ILMS are discussed. Compared with l0-LMS, the steady-state MSD of l0-ILMS is lower and less sensitive to the tuning parameters and measurement noise power. Numerical simulations comparing the performance of standard LMS, l0-LMS and l0-ILMS demonstrate the effectiveness of l0-ILMS.

中文翻译：

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用于稀疏系统辨识的改进ℓ0范数约束LMS算法的稳态均方偏差分析

摘要 l0-范数约束LMS（l0-LMS）算法是广泛用于稀疏系统识别的稀疏LMS算法之一，与其他前驱体相比，其性能相当有吸引力。然而，l0-LMS在稀疏系统辨识的最优参数选择和估计系数精度方面仍面临一些限制。在本文中，我们提出了一种改进的 l0-LMS (l0-ILMS) 算法来解决这些限制。讨论了l0-ILMS最优稳态均方偏差(MSD)的收敛条件和参数选择规则。与 l0-LMS 相比，l0-ILMS 的稳态 MSD 更低，对调谐参数和测量噪声功率不太敏感。比较标准 LMS 性能的数值模拟，