Predicting Detection Range of Maritime Sensors: a Meteorological Data-Driven Approach

Modern maritime surveillance systems integrate various RF sensors to construct regional awareness pictures. Such sensors include the Automatic Identification System, coastal and over-the-horizon radars, or passive signal analysis systems. However, these technologies exhibit inherent limitations due to variable propagation conditions. This paper introduces a novel data-driven approach that combines both meteorological and AIS detection range datasets. Standard and recurrent neural networks were implemented to predict detection ranges for AIS maritime receivers. By leveraging 72 hours of data, the models forecast the AIS discovery performance for the subsequent 24 hours with an accuracy of almost 89%. The proposed methodology offers benefits such as detecting AIS spoofing, jamming, and hidden activities, thus enabling efficient resource allocation and optimizing data acquisition strategies. This research establishes an innovative AI-driven analysis for predicting maritime sensor detection ranges, contributing to enhanced maritime security and operational effectiveness.