A fast fixed-point algorithm for independent component analysis of complex valued signals.
A fast fixed-point algorithm for independent component analysis of complex valued signals.
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DOI：
10.1142/s0129065700000028
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发表时间：
2000-02-01
影响因子：
8
通讯作者：
Hyvarinen, A
中科院分区：
文献类型：
--
作者：
Bingham, E;Hyvarinen, A
复值信号的分离是信号处理中经常遇到的问题。例如，卷积混合源信号的分离涉及到复值信号的计算。本文假设原始的复值源信号在统计上是相互独立的，并采用独立分量分析（ICA）模型来解决该问题。ICA是一种将观测到的多维随机向量转换成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不动点型算法，并通过仿真验证了该算法的计算效率。并证明了该算法给出的估计量的局部相合性。
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.