A data-driven approach for reconstruction of damaged AIS messages

Automatic Identification System is a telecommunication system that enables ships to communicate with each other. Exchanged AIS messages contain critical information for early ship collision detection, such as vessel’s position, speed, course, etc. However, due to some technical limitations of AIS (for example, a packet collision phenomenon), some fragments of AIS messages might get damaged during the transmission. In order to maintain the usefulness of AIS data, that corresponds to the maritime safety and security, the reconstruction of damaged AIS data is necessary. This paper discusses a dedicated 3-stage machine learning based framework for AIS data reconstruction. The utilized models learn the relationship between the features in AIS data, which allows for the detection of the abnormal (i.e. damaged) fields of AIS messages and prediction of their correct values. The aim of the paper is to provide detailed insights into the performance of models on the prediction stage, which is the final stage of the proposed framework. The computational experiment, conducted using real AIS data, involved examination of the impact of the position of the damaged bits in a message and the duration of data capturing on the reconstruction quality.