l Norm Constraint LMS Algorithm for Sparse System Identification

• Published in: IEEE signal processing letters Vol. 16; no. 9; pp. 774 - 777
• Main Authors: Yuantao Gu, Jian Jin, Shunliang Mei
• Format: Journal Article
• Language: English
• Published: IEEE 01.09.2009
• Subjects: Acceleration; Adaptive algorithm; adaptive filter; Adaptive filters; Clustering algorithms; Computational complexity; Computational modeling; Convergence; Cost function; l_{0} norm; least mean square (LMS); Least squares approximation; sparsity; System identification
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2009.2024736

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.