声矢量阵张量分解MUSIC算法

矢量水听器同时、共点测量声场中的声压和振速分量，因此相对于声压水听器能够获取更多的声场信息，多重信号分类算法(MUSIC)是一种具有高分辩能力的方位估计算法，本文对声矢量阵接收信号三阶张量建模，并通过高阶奇异值分解得到信号张量子空间，从而结合MUSIC算法对声源进行方位估计。基于三阶张量奇异值分解得到的信号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，因此方位估计精度更高。计算机仿真结果表明:矢量阵张量分解MUSIC算法性能优于传统矢量阵MUSIC方法。 

Tensor-decomposition MUSIC Algorithms for Vector Hydrophone Array

Vector hydrophone measures the acoustic pressure and acoustic particle velocity of the same point simultaneously, so more acoustic information is available than that of traditional scalar hydrophones. Multiple Signal Characterization is a spectral estimation algorithm with high resolution. In this paper, The 3rd tensor of the received signals from vector hydrophones are modeled, and the signal subspace is derived by the higher-order singular decomposition, so the DOA of sources are estimated using MUSIC. 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 exploited the structure inherent in the multi dimensional measurement data, so significant improvement estimation of DOA are achieved by this method. Simulation results exhibit the superiority of tensor-decomposition MUSIC algorithm to the conventional MUSIC using vector hydrophone array, so it processed high value of engineering application.