基于集员滤波的二阶Volterra自适应归一化最小平均P范数算法

doi:10.3724/SP.J.1087.2013.01780

计算机应用 ›› 2013, Vol. 33 ›› Issue (06): 1780-1786.DOI: 10.3724/SP.J.1087.2013.01780

基于集员滤波的二阶Volterra自适应归一化最小平均P范数算法

李飞祥¹,赵知劲¹,²,赵治栋¹

1. 杭州电子科技大学通信工程学院,杭州 310018
2. 中国电子科技集团第36研究所通信信息控制和安全技术国家级重点实验室, 浙江嘉兴 314001

收稿日期:2012-12-28 修回日期:2013-02-05 出版日期:2013-06-01 发布日期:2013-06-05
通讯作者: 李飞祥
作者简介:李飞祥(1987-)，男，河南安阳人,硕士研究生，主要研究方向：自适应滤波算法;赵知劲(1959-)，女，浙江宁波人，教授，博士生导师，主要研究方向：自适应信号处理、通信与语音信号处理；赵治栋（1976-），男，山东泰安人，副教授，博士，主要研究方向：信号处理、嵌入式系统。
基金资助:
国家自然科学基金资助项目(60872092)

Set-membership normalized least mean P-norm algorithm for second-order Volterra filter

LI Feixiang¹,ZHAO Zhijin¹,²,ZHAO Zhidong¹

1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China
2. State Key Lab of Information Control and Security Technology in Communication of No.36 Research Institute, China Electronic Technology Corporation, Jiaxing Zhejiang 314001, China

Received:2012-12-28 Revised:2013-02-05 Online:2013-06-05 Published:2013-06-01
Contact: LI Feixiang

摘要/Abstract

摘要： 针对Volterra非线性滤波算法计算复杂度呈幂级数增加的问题，提出了一种α稳定分布噪声下的基于集员滤波的二阶Volterra自适应滤波新算法。由于集员滤波的目标函数考虑了所有输入和期望输出的信号对，通过误差幅值的p次方的门限判决，更新Volterra滤波器的权向量，不仅有效降低了算法复杂度，而且提高了自适应算法对输入信号相关性的鲁棒性；并推导给出了权向量的更新公式。仿真结果表明，该算法计算复杂度低、收敛速度快，对噪声及输入信号相关性有较强的鲁棒性。

关键词: 集员滤波, Volterra滤波器, α稳定分布噪声, 相关性, 复杂度

Abstract: In allusion to the problem that the computational complexity of Volterra for nonlinear adaptive filtering algorithm increases in power series, a second-order Volterra adaptive filter algorithm based on Set-Membership-Filtering (SMF) under the α-stable distributions noise was proposed. As the object function of SMF involved all signal pairs of input and output, through the threshold judgment of the p square of output errors amplitude the weight vectors of Volterra filter were updated, not only reducing the complexity of filtering algorithm, but also improving the robustness of the adaptive algorithm for input signal correlation. And the update formula of the weight vectors was derived. The simulation results show that the proposed algorithm has lower computational complexity, faster convergence rate, and better robustness against the noise and the input signal correlation.

Key words: Set-Membership Filtering (SMF), Volterra filter, α-stable distribution noise, correlation, complexity

中图分类号:

TN911.72

李飞祥赵知劲赵治栋. 基于集员滤波的二阶Volterra自适应归一化最小平均P范数算法[J]. 计算机应用, 2013, 33(06): 1780-1786.

LI Feixiang ZHAO Zhijin ZHAO Zhidong. Set-membership normalized least mean P-norm algorithm for second-order Volterra filter[J]. Journal of Computer Applications, 2013, 33(06): 1780-1786.

参考文献

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基于集员滤波的二阶Volterra自适应归一化最小平均P范数算法

Set-membership normalized least mean P-norm algorithm for second-order Volterra filter

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