软件导刊2024，Vol.23Issue(10)：66-72,7.DOI:10.11907/rjdk.231913
基于CNN-F-LSTM-Attention的船舶轨迹预测
Ship Trajectory Prediction Based on CNN-F-LSTM-Attention
摘要
Abstract
With the acceleration of economic globalization and the continuous expansion of international trade,the maritime transportation in-dustry is developing rapidly.In ports with high traffic density and complex conditions,traffic safety management is facing enormous challeng-es.Ship collision is one of the frequent types of accidents at sea,and accurate ship prediction is extremely important for maritime traffic man-agement and ensuring the safety of ship navigation.The commonly used method for predicting ship trajectories is the Long Short Term Memory Network,but it has a large number of gate control weight parameters,a complex structure,and insufficient exploration of spatial and temporal features.A ship trajectory prediction model combining convolutional neural network,improved long short-term memory network,and attention mechanism is proposed to address the above issues.This model reduces structural complexity,improves training speed and generalization per-formance through an improved long short-term memory network;At the same time,convolutional neural networks are introduced to fully ex-plore the spatial and temporal features of trajectory data,and different weights are assigned to different features through attention mechanisms to filter out useless feature information and improve model accuracy.The experimental results on real datasets show that the proposed model has improved accuracy in predicting latitude,longitude,heading,and speed compared to mainstream control models.关键词
轨迹预测/数据预处理/深度学习/自然语言处理/AIS数据Key words
track prediction/data preprocessing/deep learning/natural language processing/AIS data分类
信息技术与安全科学引用本文复制引用
王雨晴,李修来,刘笑嶂,邹少华..基于CNN-F-LSTM-Attention的船舶轨迹预测[J].软件导刊,2024,23(10):66-72,7.基金项目
海南省重点研发计划项目(ZDYF2022GXJS348) （ZDYF2022GXJS348）
