冲击噪声下的LMS和RLS联合滤波算法

doi:10.3969/j.issn.1001-2400.2017.02.028

西安电子科技大学学报

冲击噪声下的LMS和RLS联合滤波算法

杨威;刘宏清;黎勇;周翊

(重庆邮电大学重庆市移动通信技术重点实验室，重庆 400065)

收稿日期:2016-04-13 出版日期:2017-04-20 发布日期:2017-05-26
作者简介:杨威(1990-), 男，重庆邮电大学硕士研究生，E-mail: klyangwei@outlook.com
基金资助:
国家自然科学基金资助项目(61501072, 61401050)；重庆市基础与前沿研究计划资助项目(cstc2014jcyjA40017, cstc2014jcyjA40027, cstc2015jcyjA40027)

Joint estimation algorithms based on LMS and RLS in the presence of impulsive noise

YANG Wei;LIU Hongqing;LI Yong;ZHOU Yi

(Chongqing Key Lab. of Mobile Communications Technology, Chongqing Univ. of Posts and Telecommunications, Chongqing 400065, China)

Received:2016-04-13 Online:2017-04-20 Published:2017-05-26

摘要/Abstract

摘要：

为解决传统自适应滤波算法在冲击噪声下性能显著下降的问题，提出了新的适用于冲击噪声环境下的自适应联合滤波算法．研究发现，冲击噪声具有在有限的时间内呈现较大的幅度，而在其他时间内的幅度值则很小的近似稀疏特性．利用冲击噪声的这个特点重新构造目标函数，设计出信号/噪声的联合估计算法，该算法利用噪声的结构特性抑制噪声．仿真结果表明，提出的算法较非联合的p范数滤波算法提高了收敛速度，并减小了稳态误差，整体性能更优越．

关键词: 自适应算法, 稀疏性, 冲击噪声, 联合估计

Abstract:

In order to solve the problem that the traditional adaptive algorithms are not able to perform well under the impulsive noise case,this paper develops new adaptive filtering algorithms in the presence of impulsive noise. A close inspection of the impulsive noise reveals that the noise has the sparse property in the time domain because it contains few large values and lots of small values in amplitude.By reformulating the cost functions utilizing this feature of noise into traditional adaptive algorithms,joint sparse online estimation algorithms are developed.The proposed algorithms exploit the noise structure to better suppress the noise.The results demonstrate the superior performance of the proposed methods compared to the existing p-norm algorithms in terms of convergence speed and steady-state error.

Key words: adaptive algorithms, sparsity, impulsive noise, joint estimation

杨威;刘宏清;黎勇;周翊. 冲击噪声下的LMS和RLS联合滤波算法[J]. 西安电子科技大学学报, 2017, 44(2): 165-170.

YANG Wei;LIU Hongqing;LI Yong;ZHOU Yi. Joint estimation algorithms based on LMS and RLS in the presence of impulsive noise[J]. Journal of Xidian University, 2017, 44(2): 165-170.

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冲击噪声下的LMS和RLS联合滤波算法

Joint estimation algorithms based on LMS and RLS in the presence of impulsive noise

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