Prediction of Lightweight AIS-Based Ship Trajectories with Spline Interpolation

Automatic identification system(AIS) of ships provides a large amount of real-time ship navigation data, which has become an indispensable key data source in many fields such as maritime traffic management, search and rescue operations, and risk assessment. Among them, ship trajectory prediction has received wide attention. However, realizing accurate long-time trajectory prediction faces two major problems: One is the integrity of AIS data itself, and the other is the efficiency of prediction models. Therefore, how to effectively deal with the missing AIS data and how to construct a lightweight and efficient prediction model have become the key problems to be solved. In this paper, a lightweight AIS-based ship trajectory prediction method with spline interpolation was proposed. Spline interpolation was used to fill in the missing AIS data, and a lightweight linear layer structure was introduced to reduce the complexity of the deep learning model. The experimental results show that the method can effectively interpolate the missing AIS data, significantly reduce the number of parameters and computation of the deep learning model, and then improve the prediction accuracy of ship trajectories.