基于反双曲正弦函数的抗冲激块稀疏自适应滤波算法

doi:10.11772/j.issn.1001-9081.2017.01.0197

计算机应用 ›› 2017, Vol. 37 ›› Issue (1): 197-199.DOI: 10.11772/j.issn.1001-9081.2017.01.0197

基于反双曲正弦函数的抗冲激块稀疏自适应滤波算法

魏丹丹, 周翊, 师黎明, 刘宏清

信号与信息处理重庆市重点实验室(重庆邮电大学), 重庆 400065

收稿日期:2016-08-12 修回日期:2016-09-12 出版日期:2017-01-10 发布日期:2017-01-09
通讯作者: 魏丹丹
作者简介:魏丹丹(1991-),女,贵州遵义人,硕士研究生,主要研究方向:语音信号处理、回声消除;周翊(1974-),男,四川成都人,教授,博士,主要研究方向:语音信号处理;师黎明(1989-),男,河南漯河人,博士研究生,主要研究方向:数字信号处理、盲源分离;刘宏清(1980-),男,黑龙江佳木斯人,教授,博士,主要研究方向:稀疏信号处理。
基金资助:
国家自然科学基金资助项目（61501072）；重庆市科委自然科学基金资助项目（cstc2015jcyjA40027）；重庆邮电大学自然科学基金资助项目（A2015-60）。

Block-sparse adaptive filtering algorithm based on inverse hyperbolic sine function against impulsive interference

WEI Dandan, ZHOU Yi, SHI Liming, LIU Hongqing

Chongqing Key Laboratory of Signal and Information Processing(Chongqing University of Posts and Telecommunications), Chongqing 400065, China

Received:2016-08-12 Revised:2016-09-12 Online:2017-01-10 Published:2017-01-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61501072), the Natural Science Foundation of Chongqing Science and Technology Commission (cstc2015jcyjA40027), the Natural Science Foundation of Chongqing University of Posts and Telecommunications (A2015-60).

摘要/Abstract

摘要： 针对现有基于最小均方误差（MSE）的块稀疏系统辨识算法抗冲激性能不佳的问题，提出了一种利用反双曲正弦函数替代最小均方误差的改进型块稀疏归一化最小均方（IBS-NLMS）算法。该算法首先构造新的代价函数，利用负梯度最陡下降法求出增量，进而导出了新的滤波器权系数更新公式，在公式迭代过程中出现的冲激噪声会导致权系数的更新量趋于零向量，从而消除了由于非高斯冲激干扰而导致的算法发散问题。同时，理论分析并推导出了该算法的均值收敛过程。块稀疏系统辨识的仿真结果表明，在非高斯冲激噪声干扰和截断变化情况下，改进型算法与块稀疏归一化最小均方（BS-NLMS）算法相比有更快的收敛速度和更小的稳态误差。

关键词: 自适应滤波器, 非高斯噪声, 反双曲正弦函数, 块稀疏系统, 系统辨识

Abstract: Since the existing block-sparse system identification algorithm based on Mean Square Error (MSE) shows poor performance under impulsive interference, an Improved Block Sparse-Normalization Least Mean Square (IBS-NLMS) algorithm was proposed by introducing the inverse hyperbolic sine cost function instead of MSE. A new cost function was constructed and the additive value was obtained by steepest-descent method. Furthermore, a new vector updating equation for filter coefficients was deduced. The adaptive update of the weight vector was close to zero in the presence of impulsive interference, which eliminated the estimation error of adaptive updating based on the wrong information. Meanwhile, mean convergence behavior was analyzed theoretically and then the simulation results demonstrate that in comparison with the Block Sparse-Normalization Least Mean Square (BS-NLMS) algorithm, the proposed algorithm has higher convergence rate and less steady-state error under non-Gaussion noise impulsive interference and abrupt change.

Key words: adaptive filter, non-Gaussion noise, inverse hyperbolic sine function, block sparse system, system identification

中图分类号:

TN911.72

魏丹丹, 周翊, 师黎明, 刘宏清. 基于反双曲正弦函数的抗冲激块稀疏自适应滤波算法[J]. 计算机应用, 2017, 37(1): 197-199.

WEI Dandan, ZHOU Yi, SHI Liming, LIU Hongqing. Block-sparse adaptive filtering algorithm based on inverse hyperbolic sine function against impulsive interference[J]. Journal of Computer Applications, 2017, 37(1): 197-199.

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基于反双曲正弦函数的抗冲激块稀疏自适应滤波算法

Block-sparse adaptive filtering algorithm based on inverse hyperbolic sine function against impulsive interference

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