Blind Source Separation Based on Nonlinear Autocorrolation Using LMS Algorithm

Blind source separation (BSS) is the technique that anyone can separate the original signals or latent data from their mixtures without any knowledge about the mixing process، but using some statistical properties of latent or original source signals. Independent component analysis is a statistical method expressed as a set of multidimensional observations that are combinations of unknown variables. These underlying unobserved variables are called sources and they are assumed to be statistically independent with respect to each other. In this paper we will use the nonlinear autcorrelation function as an object function to separate the source signals from the mixing signals. Maximization of this object function using the LMS algorithm will be obtained the coefficients of a linear filter which separate the source signals. To calculate the performance of the proposed algorithm، two parameters of Performance Index (PI) and Signal to Interference Ratio (SIR) will be used. To test the proposed algorithm، we will use Inovation Gaussian signals، Speech signals and ECG signals. It will be shown that the proposed algorithm gives better results than the other methods such as Newton method that has been proposed by Shi.