The commonly used blind separation algorithms include two order statistics, higher order cumulants, and maximization of information Infomax) and independent component analysis (ICA). The conditions for obtaining the best performance of these methods are always related to the hypothesis of the probability density function of the source signal. The separation performance will be greatly reduced when the assumed probability density is very different from the density function of the actual signal. In this paper, a method of kernel function density estimation for blind separation of arbitrary signal sources is proposed. The performance of several blind separation algorithms is compared with some typical examples, and the feasibility of the method is verified.

盲源分离\A Radar Anti-jamming Technology Based on Blind Source Separation.pdf
盲源分离\An ICA Based Antijamming Method for Frequency.pdf
盲源分离\bss.docx
盲源分离\一种新的基于峰度的盲源分离开关算法_牛龙.caj
盲源分离\任意信号源的盲分离_王惠刚.caj
盲源分离\任意信号源盲分离算法性能研究_杨坤德.caj
盲源分离

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