The invention relates to a ship trajectory prediction method based on a one-dimensional convolutional neural network and LSTM. The method comprises the following steps: a data preprocessing step: preprocessing trajectory data including ship position, navigational speed and course information collected by a ship AIS to obtain trajectory segmentation data; a feature extraction step: adopting a one-dimensional convolutional neural network to perform feature extraction optimization on the trajectory segmentation data, and combining the extracted advanced features with the trajectory segmentation data to construct input data of trajectory prediction training; a trajectory prediction model training step: importing the input data into an LSTM neural network model to learn a ship motion law implied in trajectory data; and a trajectory prediction step: predicting the position of the ship at the next moment by using the ship motion law. Compared with other existing prediction methods, the methodhas the advantages of better prediction precision, lower mean square error and quicker prediction.

    本发明涉及一种基于一维卷积神经网络和LSTM的船舶轨迹预测方法,包括以下步骤:数据预处理步骤:对通过船舶AIS采集的包括船位、航速和航向信息在内的轨迹数据进行预处理得到轨迹切分数据;特征提取步骤:采用一维卷积神经网络对轨迹切分数据进行特征提取优化,并将提取到的高级特征与所述轨迹切分数据结合来构造轨迹预测训练的输入数据;轨迹预测模型训练步骤:将所述输入数据导入到LSTM神经网络模型学习轨迹数据中隐含的船舶运动规律;轨迹预测步骤:利用所述船舶运动规律对船舶下一时刻的位置进行预测。本发明较其他现有预测方法具有更好的预测精度,均方误差更低,并且预测更迅捷。


    Access

    Download


    Export, share and cite



    Title :

    Ship trajectory prediction method and system based on one-dimensional convolutional neural network and LSTM


    Additional title:

    基于一维卷积神经网络和LSTM的船舶轨迹预测方法及系统


    Contributors:
    WANG BO (author) / CUI BIN (author) / MENG XIANGCHAO (author) / LIU DONGYU (author) / FEI TINGWEI (author) / GAO XIAOQIONG (author)

    Publication date :

    2021-03-26


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

    Song, Xiao / Chen, Kai / Li, Xu et al. | IEEE | 2021


    Ship Trajectory Prediction with Social History LSTM

    Zhao, Wenfeng / Zhang, Xudong | IEEE | 2023


    RF and LSTM combined ship trajectory prediction model

    ZHANG CONG / ZHU JISHUAI / DENG MEIHUAN et al. | European Patent Office | 2024

    Free access

    Aircraft Trajectory Prediction Using Social LSTM Neural Network

    Xu, Zhengfeng / Zeng, Weili / Chen, Lijing et al. | ASCE | 2021


    Aircraft Trajectory Prediction Using Social LSTM Neural Network

    Xu, Zhengfeng / Zeng, Weili / Chen, Lijing et al. | TIBKAT | 2021