A blind source separation algorithm based on the dynamic similarity of signals

Abstract

Blind source separation (BSS) refers to obtain source signals by means of their linear commixture with unknown mixing channel. Existing BSS methods mainly rely on the basic hypothesis that the source signal is non-Gaussian, which leads to its inability to separate the mixed Gaussian signals, and severely limits the application of BSS. In order to solve the above problem and the shortcomings that cosine similarity will encounter in the measurement of dynamic similarity of signals, this paper utilizes the generalized Jaccard index to quantify the dynamic similarity of signals, and then proposes the BSS method based on dynamic similarity of signals to realize the separation of nonlinear chaotic Gaussian signals. This method introduces the exponential function consisting of dynamic stationarity factor and independence factor of signal as the cost function of blind source separation, and then employs the imperialist competition algorithm with fast convergence speed to solve cost function to achieve the separation of signal. The effectiveness of proposed method is verified by conducting simulation experiments on the synthesized nonlinear chaotic Gaussian signals and ECG signals. Simultaneously, in comparison with the BSS method based on cosine index, the experimental results show that the proposed method has smaller cross-talking error and better separation effect.