Deep learning approaches for AIS data association in the context of maritime domain awareness

摘要

Automatic Identification System (AIS) allows the ships to broadcast important kinematic and static information, and is one of the most commonly used tools for ship traffic monitoring as individual tracks can be inferred from chronological sequence of the ships' AIS messages. In this paper, we propose two deep learning based methods for AIS data association. The first method predicts a ship's position at the time of a new message and then computes the association probability. The second method computes the association probability directly without ship position interpolation. Both methods use only three AIS data attributes for inference: longitude, latitude and time. We validate the proposed methods' performance with real AIS dataset and show that they achieve reliable data association.