Mixing Matrix Estimation in Blind Source Separation Based on Generalized Gaussian Mixture Modal

Abstract

The accurate estimation of mixing matrix is critical for blind separation, for solving the problems of traditional methods such as bad robustness and low accuracy, a method based on statistical modal is proposed. The generalized Gaussian mixture modal is used to fit the distribution of single-source-points, a new objective function for clustering is obtained from the view of maximum likelihood estimation. Constrained particle swarm optimization is used to optimize the objective function, by which the mixing matrix is estimated. This method is applicable to determined and underdetermined blind source separation. The simulation shows that the proposed method has higher estimation accuracy and is more robust than traditional methods.