Introduction - If you have any usage issues, please Google them yourself
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.