A blind source separation technique using second-order statistics
A blind source separation technique using second-order statistics
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DOI：
10.1109/78.554307
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发表时间：
1997-02-01
影响因子：
5.4
通讯作者：
Moulines, E
中科院分区：
文献类型：
--
作者：
Belouchrani, A;AbedMeraim, K;Moulines, E
源的分离包括恢复一组信号，其中只观察到瞬时线性混合。在许多情况下，没有关于混合矩阵的先验信息：线性混合应该被“盲”处理。这通常发生在窄带阵列处理应用中，当阵列流形是未知的或失真。本文介绍了一种新的源分离技术，利用源信号的时间相干性。与其他以前报道的技术相比，所提出的方法只依赖于固定的二阶统计量，是基于一组协方差矩阵的联合对角化。对该方法进行了渐近性能分析，并通过数值仿真验证了该方法的有效性。
Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: The linear mixture should be ''blindly'' processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or distorted.This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.