A prediction method of vessel trajectory based on Encoder-Decoder LSTM
Accurately predicting vessel trajectory is crucial for early warning and safe navigation,yet accuracy and stability remain major need to be solved at present.To remedy this,a vessel trajectory prediction method based on an Encoder-Decoder LSTM neural network is proposed.Firstly,vessel AIS trajectory data is preprocessed using methods such as denoising,segmentation,interpolation,stay point detection,and normalization to extract vessel sailing trajectories.Next,a vessel trajectory prediction model based on the Encoder-Decoder LSTM architecture is constructed,and the model parameters are initialized.Finally,the proposed model is trained and validated using real AIS data of ferries in the Tianshenggang waters in the Jiangsu section of the Yangtze River and compared with other widely-used trajectory prediction models.The results shows that this method can achieve accurate prediction of trajectories,and the predicted trajectories have a significant reference value.
国家科技期刊平台
NSTL
万方数据
