Underdetermined blind identification based on charrelation matrix and tucker decomposition

Abstract

A novel underdetermined blind identification algorithm based on charrelation matrix and tucker decomposition is developed. The charrelation matrix is a generalization of covariance matrix, encompassing statistical information beyond second-order while maintaining a convenient 2-dimensional structure. The core functions of charrelation matrices in different processing-points are stacked as a three-order tensor, and then tucker decomposition of tensor is executed to estimate the mixing matrix. Theoretical analysis and simulation results illustrate the proposed algorithm perform better than the representative underdetermined blind identification algorithm based CP (CANDECOM/PARAFAC) decomposition in computational complexity and identification performance.