| Citation: | ZHAO R J，YANG W，WU Z Y，et al. Ship track prediction method based on LSTM and nautical chart constraints[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（10）：3424-3432 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0516
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To address the issues of insufficient trajectory feature extraction, low prediction accuracy, and stability in existing methods for ship trajectory prediction, especially for military ships with sparse points and flexible maneuvering characteristics, this paper proposed an improved long short-term memory (LSTM) artificial neural network ship position prediction method. This approach was based on automatic identification system (AIS) data, considering the multi-dimensional features of the trajectory, inter-trajectory correlation features, and nautical chart constraints (NCC) for ships sailing at sea. For military ship trajectories, historical trajectories were interpolated by cubic spline interpolation to generate equidistant point data for prediction. The navigation area map was rasterized, with navigable grids defined to establish map constraints and improve prediction accuracy. Finally, when designing the LSTM-based network, chart constraints were integrated into the model training and prediction process by using a custom loss function, grid matching for predicted points, and other methods. Simulation results based on AIS data from the South China Sea show that the proposed network can effectively predict ship trajectories, especially for military ships with high maneuverability. The proposed method outperforms traditional prediction methods in both prediction accuracy and stability.
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