Research on Ship Trajectory Prediction Algorithm Based on Long Short Term Memory Network

A ship trajectory prediction algorithm based on LSTM network is proposed to address practical issues such as low accuracy of ship trajectory prediction, slow warning response, and susceptibility to collision accidents. Firstly, utilizing the advantages of LSTM network in processing time series data, we design and construct a trajectory prediction model, and provide the program for implementing the algorithm; Secondly, the longitude, latitude, ship speed, heading, and timestamp in AIS data are called as sample features, and filtering, linear interpolation, and standardization methods are used to preprocess the data. Using the Python library for deep time series modeling and the PaddlePaddle learning framework, a high-precision ship navigation trajectory prediction learning model is established for deep learning training; Finally, accurate prediction of ship trajectory is achieved through data model analysis and calculation. Experimental analysis shows that the algorithm can achieve accurate ship collision avoidance and route prediction, effectively improving the ship's ability to respond to maritime risks.