l0 Norm constraint LMS algorithm for sparse system identification 论文

摘要

Abstract—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 norm—a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This inte-gration 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 par-tial 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. Index Terms— norm, adaptive filter, least mean square (LMS), sparsity. I.