基于核密度最大熵方法的杂系混合信号盲分离*

传统的独立分量分析方法普遍存在的非线性评价函数只能凭经验选取，当混合信号同时包含超高斯和亚高斯信号时，算法难以取得很好的分离效果。利用基于随机变量矩的核密度最大熵方法对非线性函数进行直接估计，提出了基于核密度最大熵方法的杂系混合信号盲分离算法，成功地分离了杂系混合信号。仿真结果验证了算法的有效性。

Blind separation of hybrid mixture signals based on kernel density maximum entropy approach

In some traditional methods of independent component analysis, the nonlinear evaluation functions are always chose empirically for demixing mixture signals.There is a dramatic degradation of the performance of these methods when the mixture contains super-Gaussian and sub-Gaussian ones at one time.Kernel density maximum entropy method(KD-MEM)is a parametric probability density estimated based on moments of random variables.In this paper,by use of kenel density maximum entropy approach(KD-MEM),directly evalu...