Heterogenous Multi-Source Fusion for Ship Trajectory Complement and Prediction with Sequence Modeling

Abstract

Effectively monitoring ships and discovering abnormal ship trajectory in time is necessary for marine traffic supervision. The basic work of discovering the ship’s abnormal trajectory is to predict the ship’s navigation dynamically. Previous works in ship trajectory prediction are basically concern on single-source data, for example, the AIS data. These methods ignore the relations between different sources which may improve the performance of predicting ship trajectory. We propose a neural sequence model based on heterogeneous multisource fusion for ship trajectory completion and prediction. Our method makes better utilization of AIS, GPS and ARPA radar information to predict ship trajectory precisely. We construct a dataset which contains about 8 million ship trajectory samples and the experiments demonstrate that our multi-source fusion model gains promising results.