基于多项式卡尔曼滤波的船舶轨迹预测算法

Vessel Trajectory Prediction Algorithm Based on Polynomial Fitting Kalman Filtering

  • 摘要: 考虑到在船舶航行的实际过程中,船舶自动识别系统(AIS)设备提供的船舶运动点迹往往呈现出信息缺失、非线性、多机动的问题,导致利用AIS设备辅助海上指挥系统难以准确判断船舶位置。针对以上问题,本文在传统卡尔曼滤波理论的基础上构建多项式卡尔曼滤波器拟合非线性系统,补偿航迹定位数据信息缺失、更新较慢等问题,并基于经纬度信息预测船舶运动轨迹。结果表明,该方法实现简单且收敛迅速,能够有效解决实际过程中船舶轨迹的预测问题,满足基本的实效性与准确性,能够为相关海事部门预测船舶目的、行为提供较为可靠的辅助手段。

     

    Abstract: This paper addresses the problem of uneven trajectory distribution, nonlinearity trajectory and multi-maneuvering provided by the ship automatic identification system (AIS) equipment. Due to the such it is difficult to accurately determine the position of the ship by using the AIS equipment to assist the sea command system. Based on the traditional Kalman filter theory, a vessel trajectory prediction algorithm is carried out by constructing a polynomial Kalman filter to predict these using latitude and longitude information. The method also solves the data compensation and slower update problem. As the result of simulating the tracking effect, simple and fast as we can conclude, such method can effectively solve the problem of ship trajectory prediction in the actual process and meet the basic effectiveness and accuracy. In this case, relevant maritime department could predict the ship's purpose and behavior by taking such as a reliable auxiliary means.

     

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