AI for Radar in an Air Traffic Management System

This PhD investigates and develops Deep Learning classification and explainability methods that can be applied to a safety-critical radar system. Artificial Intelligence (AI) in civilian Air Traffic Management (ATM) is still in its infancy. Increased number of Unmanned Autonomous Vehicles (UAV) threatens the safety of both low-flying passenger jets and airports. Currently, most airport Primary Surveillance Radars (PSR) used for Air Traffic Control (ATC), do not typically perform real-time classification of aircraft radar signatures. This PhD proposes the incorporation of the deep learning (DL) architectures in the classification of air vehicles radar signatures as an automated way of mapping these signatures to discrete aircraft classes. Here, the research will create real-time explainable AI (XAI) solutions ranging from data feature based to symbolic based to explain the DL actions.