The Algorithm For Blind Signal Separation Based On The Independent Component Analysis

Blind signal separation (BSS) is a separation process that extract a series of signal source from the mixed data on the mixing process is unknown (the so-called "blind") .The noise model study with blind signal separation issue has broad application prospects. In this paper, we propose an approach to blind source separation of linear mixtures of the signals that the observations are contaminated with Gaussian noise.Firstly, we introduce the basic theory of independent component analysis, high order theory and the information theory, analyses the principle and characteristic of some classical algorithms.Blind source separation algorithm usually assume that the source signals in the prior probability density function, and thus access to critical nuclear function, and then isolated from the mixed-signal source signal. However, the assumption that the probability density function and a real probability density function different, the source will not be the correct signal separation. By the general assumption that the probability density function of the kernel function can be isolated from separate sub-Gaussian signal or super-signals. To solve this problem, this paper based on information theory, based on information of great principle and natural gradient, a super-Gaussian and mixed-signal-Blind separation method. The method combined use of Gaussian function and hyperbolic secant square function and the product of the combination of two Gaussian function of the signal source to estimate the probability density function, the peak of the information used as a parameter to choose probability density and the nonlinear model Function. But this method because of the white constraint conditions, so the number of non-stationary signals exist Stability, in order to eliminate this albino and bound algorithm to a number of non-stationary signals is unstable, and the albino relax restrictive conditions (known as incomplete constraints).In the presence of noise, the general blind separation algorithm will not get good results, in this paper, the wavelet transform and the method of combining containing Gaussian noise mixed-signal separation. First of mixed signals to deal with noise, but when a eliminate noising can not be too thorough, to the extent possible, does not damage a useful observation signal components, but the problem is that this separation signal there will be significant residual noise. In view of this situation, we are once again using the wavelet transform the separation signal to noise elimination, and thus be better signal separation, the method effectively realize the contained noise of the super-Gaussian and mixed-signal Blind Separation, through simulation proved the effectiveness of the algorithm.