浅海声速剖面与移动声源的跟踪定位  

Tracking-positioning of sound speed profiles and moving acoustic source in shallow water

作　　者：郭晓乐[1,2] 杨坤德[1,2] 马远良[1,2] 

机构地区：[1]西北工业大学海洋声学信息感知工业和信息化部重点实验室,西安710072 [2]西北工业大学航海学院,西安710072

出　　处：《声学学报》2017年第1期1-13,共13页Acta Acustica

基　　金：国家自然科学基金项目(11174235)资助

摘　　要：在水平非均匀分布的浅海环境中,针对移动声源跟踪时,声速剖面的变化会对声场产生影响,提出了一种利用集合卡尔曼滤波算法的声速剖面跟踪反演和移动声源跟踪定位的方法。首先,将声速剖面进行距离和深度的参数化表示,从而将对声速剖面的跟踪转化为对声速剖面前3阶经验正交函数系数的跟踪;其次,通过将声源状态信息和声速剖面信息表示为状态变量,而将垂直线列阵接收到的声场信息作为测量值建立状态-测量模型,然后利用集合卡尔曼滤波方法对模型状态变量进行跟踪。仿真结果得出:声速剖面跟踪反演的均方根误差和移动声源跟踪定位的绝对误差都非常小,对声源的跟踪定位精度很高。并且通过增加集合样本数、增加接收信号信噪比以及增加接收阵元数目都可以提高跟踪定位结果精度。最后,利用东海实验数据对本方法进行了验证。An ensemble Kalman filter （EnKF） approach is proposed for performing sequential tracking water column sound speed profile （SSP） using a moving acoustic source. Firstly, the SSPs are discretized in depth and range. Then, the SSPs are expressed by the empirical orthogonal functions （EOFs）. Secondly, the acoustic source state information and the first three orders of EOF coefficients are expressed as state variable, and the acoustic field information received by vertical line array are the measured values. Successively, the state variables and measured values are used to establish the state-measure model. Lastly~ EnKF is used to tracking the state variables. The simulation results show that the root mean square error and absolute error is very small, so the acoustic source tracking-positioning has a high accuracy. Moreover, increase the number of sample collection, increase the signal-to-noise ratio and increase the number of receiving arrays can improve the tracking-positioning results. The method is verified using the experimental data of the East China Sea.

关 键 词：跟踪定位 均方根误差 卡尔曼滤波方法 卡尔曼滤波算法 阵元数目 信息表示 结果精度 信号信噪比 协方差矩阵 声场分布 

分 类 号：P733.2[天文地球—物理海洋学]