Ship Track Prediction Based on AIS Data and PSO Optimized LSTM Network

Yu Liang ； Hao Zhang

《International Core Journal of Engineering》 6卷5期 (2020/05) Pp. 23-33

https://doi.org/10.6919/ICJE.202005_6(5).0005

摘要

Predicting the dynamic information of ship navigation is the basic work of ship abnormal behavior analysis, so improving the performance of prediction models is of great significance for advancing intelligent monitoring at sea.Aiming at the problem of insufficient accuracy of existing ship trajectory prediction models, a long-term and short-term memory network (LSTM) ship track prediction model based on adaptive particle swarm optimization (PSO) optimization is proposed in this paper.The particle swarm algorithm was used to optimize and improve the number of hidden layer nodes, learning rate, maximum number of iterations, and the number of input layer steps in the LSTM network, so as to obtain a better ship track prediction model.A PSO-LSTM model was constructed using AIS data, and experiments were performed using ship AIS data in the VTS waters Wusong Traffic Management Center . Comparing the experimental results with several other models, it was found that the PSO-LSTM prediction model has higher accuracy.

關鍵字

Particle Swarm Optimization (PSO) ； Long Short-Term Memory Network (LSTM) ； Ship Track Forecast

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