被动定向浮标阵卡尔曼滤波目标跟踪研究

文章研究利用被动定向浮标阵定位跟踪水下机动目标的方法，基于卡尔曼滤波(Kalman Filter, KF)原理提出一种定位跟踪滤波器的具体实现方法。该方法能够整合多枚浮标现在及过去有误差的测量数据，提高定位精度，同时连续输出水下目标运动参数估计从而锁定目标运动轨迹。该方法实现的关键在于建立水下目标与浮标阵的数学迭代运算模型，包括状态空间的动态与观测过程。由于被动定向浮标阵目标跟踪是一个非线性估计问题，而卡尔曼滤波器是线性的，因此文章设计了近似的线性观测方程以利用卡尔曼滤波来解决这个问题。通过计算机仿真研究该滤波器的跟踪效果并与最小二乘法进行比较，估计精度明显高于最小二乘法。同时通过仿真验证该滤波器可以自适应跟踪目标的非稳态运动过程。该方法在工程实践上具有一定应用前景与指导意义。

Research on Kalman filter target tracking for passive directional buoys array

In this paper, a method of using passive directional buoys array to locate and track underwater maneuvering targets is studied and a specific implementation of the positioning and tracking filter is proposed by applying Kalman filtering (KF) principle. This method can integrate the current and past error measurement data of multiple buoys to improve the positioning accuracy and continuously output the estimation of underwater target motion parameters to lock the target motion trajectory. The key to realize this method is to establish a mathematical iterative operation model of the underwater target and buoys array, including dynamic and observation processes of state space. Since passive directional buoy array tracking is a nonlinear estimation problem, and the Kalman filter is linear, an approximate linear observation equation to apply the Kalman filter to solve the problem is designed in this paper. The tracking effect of the filter is studied by computer simulation, and by comparison, the estimation accuracy is significantly higher than that of the least square method. Furthermore, simulation results show that the filter can adaptively track the unsteady motion process of the target. The method has certain application prospect and guiding significance in engineering practice.