矢量水听器目标定向融合算法仿真

Simulation of Bearing Fusion Algorithm for Vector Hydrophone

针对矢量水听器定向算法在不同目标方向和噪声情况下精度各有优劣的特点,和定向结果普遍具有一致性较差的缺点,将多传感器和多源数据处理中先进的数据融合技术引入定向算法的研究当中。采用基于方差的加权数据融合技术,将平均声强法、反正弦法和反余弦法这三种矢量定向算法有效融合,取长补短,单一算法的执行过程中也有效沿用了重复检测的优化思想。仿真实验验证,融合算法不仅可以提高目标的定向精度,而且大大降低检测结果的均方差,从而提高了定向结果的准确性和可靠性,对水下目标检测工程具有重要意义。

Many current bearing methods for the vector hydrophone show the poor performances in the measuring consistency,and the best measuring precision under some certain conditions of target directions and noise levels.The advanced data fusion technology is used for the signal processing methods of the vector hydrophone,thus,it is widely used in the multi-sensor and multi-channel data processing.The average sound intensity method,the arc sin and arc cosine function methods are combined together based on the variance weighted optimal data fusion technology.And even for the single method,the repeat testing method is also used.Simulation results show that the fusion algorithm can enhance the bearing precision and reduce the measuring mean-square deviation,so it has the important application in underwater target detection with more accurate and reliable bearing.