基于超平面法矢量的欠定盲信号分离算法  

Underdetermined Blind Source Separation Algorithm Based on Normal Vector of Hyperplane

作　　者：肖明[1] 谢胜利[1] 傅予力[1] 

机构地区：[1]华南理工大学电子与信息学院

出　　处：《自动化学报》2008年第2期142-149,共8页Acta Automatica Sinica

基　　金：国家自然科学基金(U0635001，60505005，60674033);广东省自然科学基金(04205783，05006508);科技部重大基础前期研究专项(2005CCA04100)资助~~

摘　　要：探讨欠定情况下(观测信号少于源数目)的盲信号分离.首先给出了m维超平面的法矢量的计算公式,提出了一个基于超平面法矢量的矩阵恢复算法.其次针对语音分离,提出了k源区间及其检测方法,从而使k-SCA条件下的算法推广到了非稀疏信号的盲分离.在源信号重建上,提出了一个简化l^1范数解的新算法.几个仿真实验(含语音信号实验)证实了所提出算法的性能.Discussion of blind signal separation problem under underdetermined case （i.e., the case of less observed signals than sources） is presented. First, a formula to calculate the normal vector of any hyperplane is given and a mixing matrix recovery algorithm based on the normal vector of any hyperplane is proposed. Second, for audio signal, k-source intervals are introduced and a method to detect them is proposed. So, the algorithms under the k-SCA condition are extended to blind non-sparse signal separation. To reconstruct the sources, a new algorithm is proposed to simplify the l^1-norm solution. Several experiments demonstrate the performance of the proposed algorithm.

关 键 词：欠定盲信号分离(BSS) 稀疏成分分析(SCA) 超平面聚类 法矢量 k源区间 

分 类 号：TN911.7[电子电信—通信与信息系统]