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

作　　者：李飞祥[1] 赵知劲[1,2] 赵治栋[1] 

机构地区：[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

出　　处：《计算机应用》2013年第6期1780-1782,1786,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60872092)

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。In allusion to the problem that the computational complexity of Voherra for nonlinear adaptive filtering algorithm increases in power series, a second-order Volterra adaptive filter algorithm based on Set-Membership-Filtering （SMF） under the a -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 Voherra 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.

关 键 词：集员滤波 VOLTERRA滤波器 Α稳定分布噪声 相关性 复杂度 

分 类 号：TN911.72[电子电信—通信与信息系统]