A new proportionate normalized least mean square algorithm for high measurement noise

被引：0
|
作者：
Dong, Yinxia [1 ]
Zhao, Haiquan [1 ]
机构：
[1] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Peoples R China
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC) | 2015年
关键词：
Adaptive filtering; improved proportionate normalized least mean square algorithm; sparse system; high measurement noise;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we derive a new improved proportionate normalized least mean square (IPNLMS) algorithm with unconventional minimization criterion that minimizes the summation of each squared Euclidean norm of difference between the currently updated coefficient vector and past coefficient vectors, which is called the improved IPNLMS (I-IPNLMS) algorithm. Simulation results demonstrate that the proposed I-IPNLMS algorithm has the superiority of the lower misalignment than the conventional IPNLMS algorithm in the context of sparse system identification with a low signal-noise-ratio (SNR).
引用
收藏
页码：581 / 585
页数：5
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