DBSCAN algorithm for AIS data reconstruction

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Automatic Identification System (AIS) is a telecommunication system created to allow ships to communicate with each other by exchanging messages containing information such as vessel’s ID, position, speed, heading, etc. AIS is useful in many cases, such as early ship collision detection. However, its terrestrial segment’s drawback is a relatively low range (about 74 km, roughly 40 nautical miles). Satellite Automatic Identification System (SAT-AIS) was introduced to overcome this limitation, but it suffers from its own problem known as packet collision. The satellite receives messages from multiple terrestrial cells and communication is synchronized within such cells, but not between them, thus messages got lost or damaged when they appear at the satellite at the same time. In this paper, we present a machine learning-based approach to reconstruct those missing messages and deeply investigate whether or not a density-based spatial clustering of applications with noise (DBSCAN) can be considered in the first stage of the reconstruction. The experiment focuses on findig the optimal parameters for the clustering, running it both on original and damaged data to finally ascertain that DSBCAN can distinguish individual trajectories in a dataset that can be further reconstructed.