Charrelation matrix based blind identification of underdetermined mixtures

In this paper, we propose a novel algorithm for underdetermined blind identification problem in blind source separation. The proposed algorithm is based on the charrelation matrix of observed signals. The charrelation matrix not only can be considered as a generalized second-order covariance matrix, but also incorporates higher-order information. It is significant for blind separation problem based on statistic characteristics to extract statistical information. The problem of underdetermined blind identification is converted as a tensor decomposition model. The mixing matrix can be estimated from the rank-1 terms of the tensor decomposition. Theoretical analysis and simulation results illustrate the proposed algorithm performs competitive estimation performance compared to the underdetermined blind identification algorithm based on second-order covariance and fourth-order cumulant.