基于广义协方差张量分解的欠定盲辨识算法

Underdetermined Blind Identification Algorithm Based on Generalized Covariance and Tensor Decomposition

针对欠定盲源分离中的混合矩阵估计问题,该文利用广义协方差的统计和结构性质以及塔克分解的压缩特征,提出了一种新的欠定盲辨识算法。首先基于广义协方差矩阵建立核函数,再将核函数堆叠成三阶张量模型,然后应用塔克分解求混合矩阵。该算法不仅具有优良的辨识性能,而且具有较低的实现复杂度。最后,仿真实验证明了该文算法的有效性。

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.