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PGL: A short-time model for ship trajectory prediction

Published online by Cambridge University Press:  13 January 2025

Hao Chen ,
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window]  and
Mingzhi Chen
Hao Chen
Affiliation:
Research Institute of Underwater Vehicles and Intelligent Systems, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai 200093, China
Daqi Zhu*
Affiliation:
Research Institute of Underwater Vehicles and Intelligent Systems, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai 200093, China
Mingzhi Chen
Affiliation:
Research Institute of Underwater Vehicles and Intelligent Systems, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai 200093, China
*
*Corresponding author: Daqi Zhu; Email: [email protected]
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Abstract

Ship trajectory prediction plays a critical role in collision detection and risk assessment. To enhance prediction accuracy and efficiency, a novel hybrid particle swarm optimisation (PSO) and grey wolf optimisation (GWO) long, short-term memory (LSTM) network model is proposed (PGL model). The hybrid PSO-GWO optimisation method combines the algorithm's strengths and offers improved stability and performance. The hybrid algorithm is employed to optimise the hyperparameters of the LSTM neural networks to enhance prediction accuracy and efficiency. To demonstrate the superiority of the PGL model, the LSTM, PSO-LSTM and PGL are applied to the same dataset, and then prediction performance and processing time are compared. Experimental results indicate that the proposed PGL algorithm outperforms prediction accuracy and optimisation time.

Keywords

trajectory predictionparticle swarm optimisationgrey wolf optimisationlong and short-term memory networkAIS data
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 17
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Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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