Gradient Comparator Least Mean Square Algorithm for Identi.cation of Sparse Systems with Variable Sparsity
In adaptive system identi.cation, exploitation of sparsity that may be inherent in the system leads to improved performance of the identi.cation algorithms. The recently proposed ZA-LMS algorithm achieves this by introducing a 搝ero attractor?term in the update equation that tries to pull the coef.cients towards zero, thus accelerating the convergence. For systems whose sparsity level, however, varies over a wide range, from highly sparse to non-sparse, the ZA-LMS algorithm, however, performs poorly, as it can not distinguish between the zero and the non-zero taps of the system. In this paper, we propose a modi.ed ZA-LMS algorithm for tackling the case of variable sparseness, which selectively chooses the zero attractors only for the 搃nactive?taps. The proposed method is very simple, easy to implement and well supported by simulation studies.
Bijit Kumar Das Mrityunjoy Chakraborty
Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, K Department of Electronics and Electrical Communication EngineeringIndian Institute of Technology, Kh
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）