各向同性噪声场中单矢量传感器虚源消除MUSIC测向方法

为降低声矢量圆阵方位估计方法的计算复杂度, 避免声压振速联合处理需选取观测方向的问题, 提出了一种主特征向量加权的声矢量圆阵波束域多重信号分类(MUSIC)测向方法。在信号总功率与噪声功率之比最大的优化准则下, 求解得到单矢量传感器最优的加权向量, 即主特征向量。结合主特征向量设计相应的波束形成矩阵, 并从理论上对声矢量圆阵阵元域和波束域MUSIC的目标方位估计方差进行比较。
推导了单矢量传感器的大特征值和信号功率、大特征值对应的特征向量和其导向向量之间的关系。通过仿真实验验证了基于主特征向量波束形成矩阵的有效性, 所提方法具有较低的计算复杂度和良好的方位估计性能。水池实验数据分析表明, 所提方法能够准确估计目标方位。

Acoustic vector-sensor array processing

To reduce the computational complexity of the direction of arrival (DOA) estimation method and avoid the problem of observation direction selection in the combined information processing of pressure and particle velocity, a beamspace multi-signal classification (MUSIC) direction-finding method weighted by the principal eigenvector for the circular acoustic vector-sensor array is proposed. Under the optimization criterion of maximizing the ratio of total signal power to the noise power, the optimally weighted vector for a single vector sensor (i.e. the principal eigenvector) is solved.
The beamforming matrix is designed by the principal eigenvector, and the estimation variance of MUSIC in the element space and beamspace of the acoustic vector circular array is compared theoretically. The relationships between the large eigenvalue and signal power of a single vector sensor, the eigenvector corresponding to the large eigenvalue, and its steering vector are derived. The effectiveness of the beamforming matrix based on the principal eigenvector is verified through simulation, and it is shown that the proposed method has lower computational complexity and better azimuth estimation performance. Furthermore, pool experimental results illustrate that the proposed method can accurately estimate the azimuth of the signal source.