基于alpha稳定分布的盲信号分离  

Blind source separation based on alpha stable distributions

作　　者：任静[1] 李维勤[2] 惠鏸[2] 

机构地区：[1]西安航空学院计算机工程系,西安710077 [2]西安理工大学自动化与信息工程学院,西安710048

出　　处：《计算机工程与应用》2014年第18期215-219,229,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61102061);陕西省科学技术研究发展计划项目(No.2013JC2-17);陕西省教育厅科学研究计划项目(No.2013JK1163)

摘　　要：针对当前重拖尾信号盲分离的存在问题,提出一种基于alpha稳定分布的重拖尾信号在线盲信号分离算法。离线计算标准alpha稳定分布的概率密度函数,建立标准函数库查找表;在线估计信号的alpha稳定分布的特征参数、对称参数和尺度参数,从而可以快速获得信号的概率密度函数;采用多层神经网络准确估计评价函数。仿真结果表明,该算法具有较好的分离性能和较低的计算复杂度。The convention Blind Source Separation（BSS）algorithms have a poor separating effect for the heavy-tailed signals. An online BSS algorithm for heavy-tailed signals is proposed based on the alpha stable distribution theory. The algorithm estimates the Probability Density Functions（PDFs）of the standard alpha stable distribution, and establishes the standard function library look-up table. It estimates on-line the signal characteristic parameter, symmetrical parameter and scale parameter, and therefore the PDF is obtained fast. The score function is obtained accurately by using the multilayer neural networks. The simulated results show that, the proposed algorithm has a well convergence property and a lower computational complexity.

关 键 词：ALPHA稳定分布 独立分量分析 盲信号分离 重拖尾信号 多层神经网络 

分 类 号：TN911.6[电子电信—通信与信息系统]