PERFORMANCE ANALYSIS OF L0-LMS WITH GAUSSIAN INPUT SIGNAL

摘要：

Sparse signal processing has attracted much attention in recent years. l0-LMS, which inserts a penalty of approximated l0 norm in the cost function of standard LMS algorithm, is one of the recently proposed sparse system identification algorithms. Numerical simulation results and intuitive explanations demonstrate that l0-LMS has rather small steady-state misalignment and fast convergence rate, especially with selected parameters, compared to its various precursors. In this paper, the mean square performance of l0-LMS is theoretically analyzed based on uncorrelated Gaussian input, independence assumption, and some other reasonable assumptions. We deduce the convergence condition on step-size, the steady-state mean square deviation, as well as the criterion on parameters selection. Finally, computer simulations verified the above theoretical results and confirmed the adopted assumptions hold well.

关键词： adaptive filter sparse system identification l0-LMS mean square performance uncorrelated input

作者: Guolong Su Jian Jin Yuantao Gu

作者单位: Department of Electronic Engineering, Tsinghua University,Beijing 100084, CHINA

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 235-238

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）