加权信号张量子空间拟合算法

提出了一种基于三阶张量高阶奇异值分解的声矢量阵列加权信号张量子空间拟合算法. 首先对声矢量阵接收信号进行三阶张量建模, 并通过高阶奇异值分解得到信号张量子空间, 从而结合加权信号子空间拟合算法进行空间方位谱估计. 由于基于高阶奇异值分解得到的信号张量子空间相比于传统的矩阵奇异值分解得到的信号子空间能够更好地抑制噪声, 并且体现了多维数据之间的关联关系, 因此具有更高的方位估计精度. 理论和仿真结果表明: 该方法在低信噪比、等强度不相关信号和强相关信号条件下仍具有良好的目标分辩能力和稳定性, 工程应用价值较高.

Weighted Signal Tensor Subspace Fitting Algorithm

A weighted signal tensor subspace fitting algorithm is presented for vector hydrophone array based on higher order singular value decomposition (HOSVD). In this paper, the 3rd order tensor of the received signals from vector hydrophones array is modeled at first, then the signal tensor subspace is derived from HOSVD, and lastly, the DOA is estimated with the weighted signal subspace fitting. The 3rd tensor-based signal subspace estimation via HOSVD is a better estimate of the desired signal subspace than the subspace estimate obtained by the SVD of a matrix which exploits the structure inherent in the multi dimensional measurement data. Theoretical and simulation results show that the proposed method exhibits high resolution and robustness performance under scenarios of low signal noise ratio (SNR), non-correlative and tight-correlative signals with the same power.