基于LSTM的船舶航迹预测模型  

Prediction Model of Ship Trajectory Based on LSTM

作　　者：权波 杨博辰 胡可奇 郭晨萱 李巧勤[2] QUAN Bo;YANG Bo-chen;HU Ke-qi;GUO Chen-xuan;LI Qiao-qin(Chengdu Spaceon Technology Co.Ltd.,10 th Institute of CETC,Chengdu 611731,China;School of Information and Software Engineering,University of Electronic Science and Technology,Chengdu 610054,China)

机构地区：[1]中国电子科技集团公司第10研究所成都天奥信息科技有限公司,成都611731 [2]电子科技大学信息与软件工程学院,成都610054

出　　处：《计算机科学》2018年第B11期126-131,共6页Computer Science

基　　金：国家自然科学基金青年基金(61502082)资助

摘　　要：针对海上日趋复杂的情形,提高船舶交通服务系统(Vessel Traffic Service,VTS)的决策水平迫在眉睫。针对船舶航行轨迹多维度的特点以及对船舶轨迹预测的精确度和实时性的需求,提出了结合船舶自动识别系统(Automatic Identification System,AIS)数据和深度学习的船舶航行轨迹预测方法。构造基于AIS数据的航行轨迹特征,提出了循环神经网络-长短期记忆(Recurrent Neural Networks-Long Short-Term Memory,RNN-LSTM)模型,利用广州港内的船舶AIS数据对模型进行训练,并对未来船舶航行轨迹进行预测。实验结果表明,利用RNN-LSTM模型的预测方法具有精确度高、易实现的特点,并且与传统处理方法相比,其在处理序列数据方面更具优越性。It is imperative to raise the level of decision-making for vessel traffic service(VTS)system in the light of the increasingly complex maritime circumstances.Aiming at the multidimensional characteristics of the ship’s navigation trajectory and the demand for the accuracy and the real-time prediction of the ship’s trajectory,a prediction method combining ship trajectory automatic identification system(AIS)data and deep learning was proposed.The feature expression of vessel behavior based on AIS data was established and the recurrent neural network-long short term memory(RNN-LSTM)model was proposed.The model was trained by AIS data from the Guangzhou Harbor and used to predict vessel trajectory.The results show that the method can predict the characteristics of vessel trajectory timely with acceptable accuracy.Compared with the traditional processing method,it is more superior in processing time series data.

关 键 词：循环神经网络 航迹预测 长短期记忆 船舶自动识别系统 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]