AI for Radar in an Air Traffic Management System

该博士研究和开发深度学习分类和可应用于安全关键雷达系统的可解释性方法。人工智能(AI)在民用空中交通管理(ATM)中的应用还处于起步阶段。越来越多的无人驾驶飞行器威胁着低空飞行的客机和机场的安全。目前，大多数用于空中交通管制(ATC)的机场主监视雷达(PSR)通常不会对飞机雷达特征进行实时分类。该博士建议将深度学习(DL)体系结构纳入到飞行器雷达特征的分类中，作为将这些特征映射到离散飞机类别的自动方式。在这里，研究将创建实时可解释人工智能(XAI)解决方案，范围从基于数据要素到基于符号来解释DL操作。

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.