线性混迭信号中独立源的盲提取

基于源信号统计独立的假设,提出一种基于四阶累积量的分离判据,由此得出一种可以顺序逐个盲提取独立源信号的ICA算法,算法中利用去冗余技术剔除先前已经提取的信号.计算机仿真结果表明算法的性能良好.

Blind Extraction of Independent Signals from Their Linear Mixtures

Observed signals are always the linear mixture of some independent components. Independent component analysis (ICA) is a novel technique for dealing with such a problem. Most of the existing algorithms separate individual independent sources simultaneously. In this paper, basing on the independence assumption of the original sources, we propose a new blind separating criterion, where the square of fourth-order cumulants of the sources are employed. We next develop an ICA approach which can sequentially extract independent components blindly one by one. A new deflation technique is used in this approach for removing the previously extracted signals from the mixture. Computer simulations show the validity of the proposed approach.