基于循环神经网络的船舶航迹预测  

Vessel trajectory prediction based on recurrent neural network

作　　者：胡玉可 夏维[1,2] 胡笑旋 孙海权[1,2] 王云辉 HU Yuke;XIA Wei;HU Xiaoxuan;SUN Haiquan;WANG Yunhui(School of Management,Hefei University of Technology,Hefei 230009,China;Key Laboratory of Process Optimization and Intelligent Decision-Making,Ministry of Education,Hefei 230009,China)

机构地区：[1]合肥工业大学管理学院,安徽合肥230009 [2]过程优化与智能决策教育部重点实验室,安徽合肥230009

出　　处：《系统工程与电子技术》2020年第4期871-877,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金（71671059,71521001,71871079）资助课题

摘　　要：在海事搜救、海关缉私等应用中,对目标船舶进行航迹预测是一个关键问题。为提高预测的精度和效率,提出了一种基于循环神经网络的船舶航迹预测方法,该方法包含数据预处理和神经网络预测两个部分。在数据预处理中,设计了一种基于对称分段路径距离的数据预处理方法,消除了大量冗余数据及噪声的影响;在神经网络预测中,构建了基于门控循环单元的循环神经网络模型,实现船舶位置信息精准且高效的预测。通过大量船舶自动识别系统数据进行了对比实验,实验结果证明了方法的实用性和有效性。In maritime search and rescue,customs anti-smuggling and other scenarios,it is often necessary to forecast vessels’trajectory.In order to improve the accuracy and efficiency of the prediction,a method for vessel trajectory prediction based on recurrent neural network is proposed.The method includes data preprocessing and neural network prediction.In data preprocessing,a data preprocessing method based on symmetric segmented-path distance is designed to eliminate the influence of a large number of redundant data and noise.In the prediction of neural network,the model of recurrent neural network with gated recurrent unit as the core is constructed to realize the accurate and efficient prediction of vessels’position information.Comparative experiment is made through a large number of data from the automatic identification system,and experiment results prove that the proposed method is practical and effective.

关 键 词：航迹预测 船舶自动识别系统 对称分段路径距离 门控循环单元 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]