l0 Norm Constraint LMS Algorithm for Sparse System Identification
l0 Norm Constraint LMS Algorithm for Sparse System Identification
复制标题
DOI：
10.1109/lsp.2009.2024736
复制
发表时间：
2009-09-01
影响因子：
3.9
通讯作者：
Mei, Shunliang
中科院分区：
文献类型：
--
作者：
Gu, Yuantao;Jin, Jian;Mei, Shunliang
为了提高基于最小均方（LMS）算法的稀疏系统辨识性能，利用稀疏系统的稀疏特性，提出了一种新的自适应算法。提出了一种基于l（0）范数的通用近似方法，并将其引入到LMS算法的代价函数中。这种积分方法等价于在迭代过程中增加一个零吸引子，有效地提高了稀疏系统中占主导地位的小系数的收敛速度。此外，采用部分更新方法，降低了计算复杂度。仿真结果表明，该算法可以有效地提高基于LMS的辨识算法在稀疏系统上的性能。
In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l(0) norm-a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.