Bingham E, Hyvärinen A
Neural Networks Research Centre, Helsinki University of Technology, Finland.
Int J Neural Syst. 2000 Feb;10(1):1-8. doi: 10.1142/S0129065700000028.
Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.
复值信号的分离是信号处理中经常出现的问题。例如，卷积混合源信号的分离涉及对复值信号的计算。在本文中，假设原始复值源信号在统计上相互独立，并通过独立分量分析（ICA）模型解决该问题。ICA是一种将观测到的多维随机向量转换为尽可能相互独立的分量的统计方法。本文提出了一种能够分离复值线性混合源信号的快速定点型算法，并通过仿真展示了其计算效率。此外，还证明了该算法给出的估计器的局部一致性。
