Signal Separation Based on Extended Least Squares

Abstract

In this paper, a new signal separation method is proposed in this paper to solve the problem of poor separation effect of mixed signals in strong noise environment. Based on least squares (LS), the improved optimization model is extended by Kullback-Leibler dispersion to remove the random noise. Theoretical analysis and simulation experiments show that the algorithm proposed in this paper is superior to the existing algorithm in estimating the source signal, especially when the mixed signal is completely immersed in noise, the recovery effect of the source signal is more obvious than the existing algorithm.