估计盲信源分离混合矩阵的通用方法  

General method to estimate mixture matrix in blind source separation

作　　者：张延良[1] 张伟涛[2] 杜静静[3] 

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454001 [2]西安电子科技大学电子工程学院,西安710071 [3]河南理工大学电气工程与自动化学院,河南焦作454001

出　　处：《计算机应用》2012年第9期2432-2435,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60775013;61104079);河南理工大学青年基金资助项目(Q2011-50);河南理工大学博士基金资助项目(648753)

摘　　要：混合矩阵的估计是解决盲信源分离问题的关键一步,但现有研究中缺乏一种同时适用于适定、超定及欠定情况下混合矩阵估计的通用方法。根据张量标准分解的因子矩阵和盲信源分离混合矩阵的估计均存在幅值和排列顺序的不确定性这一性质,将混合矩阵的估计转化为观测信号统计量所组成张量的标准分解问题,标准分解采用循环最小化方法,通过交替最小二乘算法实现。理论分析和仿真实验表明,所提方法可有效解决适定、超定和欠定混合矩阵的估计,是一种估计盲信源分离混合矩阵的通用方法。The estimation of mixture matrix is a key step to solve the problem of blind source separation. But there lacks a general estimation method suitable for well-determined, over-determined and under-determined mixture matrix in the existing research. Scaling and permutation ambiguities lie in both factor matrix of tensor canonical decomposition and mixture matrix in blind source separation. With this property, the estimation of mixture matrix can be transformed into tensor canonical decomposition of observed signals＇ statistics. The canonical decomposition can be implemented by cyclic minimization, with the algorithm of alternating least squares. The theoretical analysis and simulations show that the method proposed in this paper is a general method to estimate well-determined, over-determined and under-determined mixture matrix.

关 键 词：盲信源分离 欠定混合 标准分解 循环最小化 

分 类 号：TN911.7[电子电信—通信与信息系统]