Présentation G. Spadon (à distance)

Title: From extensive data fusion to a knowledge-based system for vessel signature detection


Abstract: AIS data plays a significant role in tracking vessels around the globe, with applications ranging from national defense to regional traffic planning. While AIS messages are transmitted to satellite or terrestrial antennas, vessels propagate noise in the underwater environment that hydrophones can capture. The joining of these two different data can bring greater awareness about the ocean environment but also pose a challenge in the face of the size and complexity of dealing with heterogeneous data. Deep Learning techniques, named neural networks, have shown a remarkable capability of extracting meaningful performance over vast amounts of data applied to different applications. In this sense, this research discusses a methodological framework alongside a neural network tool-set for joining such data to detect vessels based on their underwater signature.