An algorithm for blind separation of post-nonlinearly mixed sub- and super-Gaussian signals based on the results of previous work is proposed.

The performance of existing blind source separation methods is highly affected by the non-linear contrast functions that are selected according to the distribution of original signals, and the separation results are not always ideal, especially for the mixture of super-Gaussian signal and sub-Gaussian signal.

In this paper,sub-Gaussian random projection is introduced into compressed sensing(CS) theory and two new kinds of CS measurement matrix:sparse projection matrix and very sparse projection matrix are presented.

In order to separate super-Gaussian and sub-Gaussian signals,this paper uses high-and low-frequency coefficients of wavelet transform as smooth factors,then builds a signal-to-noise ratio objective function,which uses the denominator as prediction error and can be optimized to resolve separable matrix.

Study on the generation of super-Gaussian and true-random drive signals using time domain randomization;

The technique of generating super-Gaussian and quasi-random vibration exciting signals;

The performance of existing blind source separation methods is highly affected by the non-linear contrast functions that are selected according to the distribution of original signals, and the separation results are not always ideal, especially for the mixture of super-Gaussian signal and sub-Gaussian signal.