基于IPSO-BP的船舶航迹预测研究

目的 面对复杂的海上交通及密集的物流交通流，及时有效地对船舶航迹进行跟踪预测显得尤为重要，针对传统船舶航迹预测方法精确度低且效率低下的问题，提出一种改进方法。方法 在船舶自动识别系统（AutomaticIdentificationSystem,AIS）数据的基础上，建立改进粒子群算法（IPSO）与BP神经网络相结合的船舶轨迹预测模型，利用船舶历史航行轨迹数据，实现对未来船舶运动的预测。选取宁波舟山港的船舶历史轨迹数据进行实验，并将IPSO-BP模型的实验结果与其他模型进行比较。结果 不同模型航迹预测对比结果表明，IPSO-BP模型的性能较好，其预测精度较高，适用于船舶轨迹预测。结论 使用IPSO-BP模型能够更加精准地预测船舶航迹，在船舶危险预警、船舶异常监测等方面具有重要的指导作用。

Research on Ship Trajectory Prediction Based on IPSO-BP

In the face of complex maritime traffic and dense logistics traffic flow, timely and effective tracking and prediction of ship trajectories is particularly important. The work aims to propose a method to solve the low accuracy and low efficiency of traditional ship trajectory prediction methods. A ship trajectory prediction model which combined the improved particle swarm optimization (IPSO) algorithm with the BP neural network was established based on AIS data. Historical ship trajectory data were used to predict future navigation trajectories. The historical ship trajectory data of Zhoushan Port in Ningbo was selected for the experiment, and the experimental results of the IPSO-BP model were compared with other models. Through comparing the results of different model trajectory predictions, it could be seen that the IPSO-BP model had good performance and high prediction accuracy, and is suitable for ship trajectory prediction. The use of IPSO-BP model can achieve more accurate ship trajectory prediction, which has an important guiding role for future ship hazard warning, ship anomaly monitoring, and other aspects.