中文摘要：

基于概率密度非参数估计的广义高斯密度（GGD）核估计和线性独立成分分析（ICA）神经网络，本文提出了一种新的非参数ICA算法，实现了对源信号分布的全盲要求．该算法直接由观测信号样本出发，对分离信号评价函数直接估计，可以只用一种灵活评价函数分离任意的杂系混合信号，并且GGD核可以根据源信号的高阶统计性质自适应的改变以适应不同的要求，从而在一定程度上解决了ICA算法中选取估计信号评价函数的问题．模拟实验说明了所提算法的性能优越性．

英文摘要：

Independent component analysis （ICA） is a typical and important blind source separation （BSS） method. It is to recover source signals which are considered statistically independent given only the outputs of a number of sensor mixed signals. How to choose a model of the score functions of the unknown source signals is a basic and important problem in most ICA algorithm. This is usually achieved based on the parametric and nonparametric density estimation approaches. Classical ICA algorithms fit parametric models for the hidden sources. Moreover, most ICA algorithms always assume that the distribution of source signals has a priori knowledge, but this is difficult to obtain in many situations. It has been realized that the unknown distributions of hidden sources can be estimated by nonparametric methods, some authors have discussed the nonparametric ICA method, but the kernel they used was fixed which made the algorithm does not robust in some conditions. How to develop unified nonparametric ICA algorithms, which enable to separate hybrid source signals including symmetric and asymmetric distributions with self-adaptive score functions, is very important. In this paper, efficient ICA algorithm based on generalized Gaussian density （GGD） kernel is proposed using a linear ICA neural network which can separate hybrid mixture of source signals which include both symmetric and asymmetric distribution sources. The proposed ICA algorithm is able to separate the hybrid mixtures of source signals using only a flexible model. Moreover, the GGD kernel can adaptively adjust its shaping and scaling parameter according to the high order statistics of the source signals. It paves the way to wider applications of ICA methods to real world signal processing. Simulations confirm the effectiveness of the proposed algorithm.