Adaptive algorithm for sparse system identification: Zero-attracting LMS

作者：Jin Jian; Gu Yuantao^*; Mei Shunliang

来源：Journal of Tsinghua University(Science and Technology), 2010, 50(10): 1656-1659.

• Convergence performance
• Convergence speed
• Faster convergence
• Filter coefficients
• Gradient descent
• l1 norm
• LMS
• LMS algorithms
• Recursions
• Simulation result
• Sparse system
• Sparse system identification
• Sparse systems
• System identification
• System identifications
• Tracking speed

This paper presents an adaptive algorithm using the unknown system sparsity to improve the system identification performance. A gradient descent recursion of the filter coefficient vector was deduced through introducing l1 norm, which has vital relationship with sparsity, to the cost function of LMS algorithm. Adding a zero-vector-approaching correction to the tap coefficient of the filter enables faster convergence of zero coefficients (which play a main role in sparse systems), thus notably increases the convergence speed and tracking speed in system identification. Simulation results show that the algorithm evidently improves the convergence performance of both general sparse systems and clustering sparse systems, and exhibits universal applicability and excellent robustness.

• 出版日期2010
• 单位清华大学