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中文摘要：
针对盲源分离问题,将互信息理论与典型相关分析理论相结合,提出了一种基于信息典型相关分析的盲源分离算法.该算法首先利用模式搜索法求解,得到混合信号向量的线性组合与混合信号向量延迟的线性组合之间互信息最大的信息典型向量,互信息计算中的概率密度函数由高斯核密度估计.然后,将信息典型向量依次与接收的混合信号数据阵相乘,完成对源信号的逐一抽取和分离.仿真实验结果表明,该算法不仅能有效分离包含超高斯信号成分的混合信号和包含亚高斯信号成分的混合信号,还能分离同时包含这2种成分的混合信号以及病态混合信号.
英文摘要：
To solve the problem of blind source separation,a novel algorithm based on information canonical correlation analysis（ICCA）is presented by combining the theory of mutual information with canonical correlation analysis.In this algorithm,the information canonical vectors are searched out by maximizing the mutual information between the linear combination of the observed vectors and the linear combination of the delayed observed vectors.The probability density function is estimated by Gaussian kernel estimates.Then,the source signals are extracted and separated one by one by multiplying the information canonical vectors with the observed mixture.The simulation results show that this algorithm can separate the mixture signals which consist of super-Gaussian components or of sub-Gaussian components.The mixture signals including these two components and the ill mixture signals can also be separated effectively.
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