$l_{0}$ Norm Constraint LMS Algorithm for Sparse System Identification

By Yuantao Gu, Jian Jin, Shunliang Mei. IEEE Signal Processing Letters, 2009

489 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3498dce1e7a0c7d01ae2270b6aed21f9af2c7f4b