Prediction of ship trajectory based on deep learning

Abstract

The rapid development of computer technology strongly promotes the study of maritime traffic safety. The application of artificial intelligence technology makes the ship’s trajectory prediction not limited to complex physical models, and improves the generality of ship’s track prediction. To address the problems of relatively poor prediction accuracy in existing ship trajectory prediction research and high coupling of algorithms during ship navigation while the prediction model needs to be improved, this paper combines grey forecast prediction model and long and short-term memory (LSTM) neural network to establish a neural network model for ship trajectory prediction. This model can effectively increase the number of messages in LSTM model, and reduce the loss value of the model and improve the accuracy of ship track prediction. The prediction method in this paper is verified by AIS data of a ship. The results show that compared with other time series prediction algorithms, this algorithm has higher prediction accuracy.