Blind Identification of Underdetermined Mixtures Based on Charrelation Matrix

In this paper, we propose a novel algorithm for underdetermined blind identification problems in blind signal separation. The proposed algorithm is based on the charrelation matrix of observations. The charrelation matrix can not only be considered as a generalized 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 is estimated from the rank-1 terms of the tensor decomposition. Theoretical analysis and simulation results illustrate that the proposed algorithm performs better estimated performance than the underdetermined blind identification algorithm based on second-order covariance and four-order cumulant respectively. Key-words: blind identification; blind source separation; tensor decomposition