基于张量子空间的信号参数估计算法

针对低信噪比条件下多通道信号特征参数估计问题，提出了两种基于张量子空间的信号参数估计算法，分别是基于矩阵堆叠的张量分解算法和基于张量矩阵因子的联合算法。通过研究参数化信号模型、信号子空间旋转不变性和张量范德蒙分解原理，分析了多通道数据的三维张量数据模型的构建；矩阵堆叠的张量分解算法验证了张量高阶奇异值分解是矩阵奇异值分解的推广，矩阵因子联合算法进一步提高了低信噪比条件下的信号参数估计精度。仿真以信号频率和初相位的估计精度为衡量指标，验证了低信噪比的条件下，张量子空间信号参数估计算法要优于传统的矩阵子空间信号参数估计算法。

Parameter Estimation Algorithm Based on Tensor Subspace

Two methods of parameter estimation algorithm based on tensor subspace are proposed to improve the accuracy of signal parameter estimation in low signal|to|noise ratio, which are the high order singular value decomposition algorithm based on matrix stack and the algorithm based on tensor decomposition factor. According to the parametric signal model, the shift invariance property of the signal subspace and the principle of tensor Vandermonde decomposition, the construction forms of tensor model are discussed. The high order singular value decomposition algorithm based on matrix stack verifies that the tensor high order singular value decomposition is based on the matrix singular value decomposition. The algorithm of tensor decomposition factor may further improve the accuracy of signal parameter in the case of low signal|to|noise ratio. The accuracies of frequency and phase may serve as an index of the algorithms. The simulations verify that the algorithm based on tensor decomposition is superior to traditional matrix|based decomposition algorithm in low signal|to|noise ratio.