摘要
盲源分离的目的在于只利用接收数据把被瞬时线性混合的源信号恢复出来,该文讨论的是一种在复各向同性的SαS噪声中的盲源分离方法,SαS过程能够很好地描述许多具有冲激特性的信号和噪声,但其二阶和高阶统计量是不存在的,所以首先用基于子空间逼近和白化的方法对观测数据进行处理,然后利用特征矩阵近似联合对角化方法来估计源信号和混合矩阵。仿真结果说明该方法具有良好的性能。
Blind separation of sources consists of recovering a set of signals of which only instantaneous linear mixing is observed. This paper presents a novel blind source separation method when noises are complex isotropic SaS process. SaS processes can describe many signals and noises with impulsive nature, but its second order and high order statistics are infinite, so, a subspace approach is used to process the observed data, then joint approximate diagonalization of eigen-matrices is used to estimate the mixing matrix and source signals. Computer simulation shows the high performance of the proposed method.
出处
《电子与信息学报》
EI
CSCD
北大核心
2003年第7期896-900,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(批准号 69972037
60073038)
关键词
脉冲噪声
盲源分离方法
SαS过程
联合对角化
信号子空间
Blind source separation, Symmetric α-Stable(SαS) process, Joint diagonaliza- tion, Signal subspace
