Underdetermined Blind Identification Algorithm Based on Generalized Covariance and Tensor Decomposition

In view of the estimation problem of mixing matrix in the underdetermined blind source separation (UBSS), a novel underdetermined blind identification algorithm is proposed. This proposed algorithm employs the statistical and structure properties of generalized covariance and the compressive characteristic of Tucker decomposition. Firstly, the core functions are built based on generalized covariance matrix. Then the core functions are stacked as a three-order tensor, and the tucker decomposition of constructed tensor is executed to estimate the mixing matrix. The proposed algorithm has not only the better identification performance, but also the lower computational complexity. At last, the simulation experiments demonstrate the effectiveness of the proposed algorithm.