AI for Radar in an Air Traffic Management System

空中交通管理系统中的雷达人工智能

• 批准号: 2535755
• 金额: --
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2535755
• 关键词: AI; Radar; Air; Traffic; Management

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.

该博士研究并开发了可以应用于安全关键雷达系统的深度学习分类和解释性方法。平民交通管理（ATM）中的人工智能（AI）仍处于起步阶段。无人自动驾驶汽车（UAV）的数量增加会威胁到低空乘客的飞机和机场的安全。目前，用于空中交通管制（ATC）的大多数机场主要监视雷达（PSR）通常不会对飞机雷达签名进行实时分类。该博士建议将深度学习（DL）体系结构纳入空气车雷达签名的分类中，作为将这些签名映射到离散飞机类的自动化方式。在这里，该研究将创建实时可解释的AI（XAI）解决方案，从基于数据功能到基于符号的符号来解释DL动作。