Tensor-Based Underdetermined Blind Identification of Instantaneous Mixtures

Abstract

In order to explore the effective statistical information of observed signals to stack into a tensor, while reduce the computational complexity and speed up the convergence, a new tensor-based underdetermined blind identification method of instantaneous mixture is proposed in this brief. Firstly, the autocovariance matrix of each observed sub-block is calculated to construct the symmetric third-order tensor according to segmentation scheme. Secondly, the original tensor is reduced into a low-rank kernel tensor by truncated multi-linear singular value decomposition (MLSVD). Finally, an enhanced line search (ELS) technique is applied to speed up the convergence of alternating least squares (ALS) to identify the mixing matrix. The simulation results indicate that the presented approach provides a superior estimated performance compared to the existing methods in both real and complex scenarios.