α稳定分布噪声下基于梯度范数的VSS-NLMP算法  

Gradient-norm based VSS-NLMP algorithm in α-stable environments

作　　者：郝燕玲[1] 单志明[1] 沈锋[1] 

机构地区：[1]哈尔滨工程大学自动化学院

出　　处：《系统工程与电子技术》2012年第4期652-656,共5页Systems Engineering and Electronics

基　　金：国家自然科学基金(61102107;61001154;60704018);中国博士后科学基金(20100480979)资助课题

摘　　要：针对α稳定分布噪声环境下的自适应滤波问题,提出一种新的基于梯度范数的变步长归一化最小平均p范数(variable step-size normalized least mean p-norm,VSS-NLMP)算法。该算法首先对梯度矢量进行加权平滑,以减小梯度噪声的影响,然后利用梯度矢量能够跟踪自适应过程的均方偏差这一特点,利用梯度矢量的欧氏范数控制步长的变化。给出了新算法的迭代过程,然后对其收敛性进行分析,仿真结果表明本算法较现有变步长NLMP算法有更好的性能。According to the problem of adaptive filtering in α stable environments,a gradient-norm based variable step-size normalized least mean p-norm（VSS-NLMP） algorithm is proposed.The squared norm of the smoothed gradient vector,which can track the variation of the mean square deviation at iteration,is used to update the step-size parameter in the algorithm.The weighted average of the gradient vector reduces the noise effectively and results in a more stable and less noisy adaptation of the step-size parameter.The update and convergence of the proposed algorithm are formulated.The simulation results indicate that the proposed algorithm has a better performance compared with the existing VSS-NLMP algorithms.

关 键 词：信号处理 Α稳定分布 分数低阶统计量 自适应滤波 变步长归一化最小平均p范数算法 

分 类 号：TN911.7[电子电信—通信与信息系统]