论文标题
GPS欺骗检测的深度学习启用了无人驾驶飞机系统
Deep Learning for GPS Spoofing Detection in Cellular Enabled Unmanned Aerial Vehicle Systems
论文作者
论文摘要
基于蜂窝的无人机（UAV）系统是一个有前途的范式，可为无人机操作提供可靠和快速的视觉线（BVLOS）通信服务。但是，这种系统面临着严重的GPS欺骗无人机的位置。为了启用安全无人机导航BVLOS，本文提出了一个蜂窝网络辅助无人机位置监控和反GPS欺骗系统，其中使用深度学习方法来实时检测欺骗的GPS位置。具体而言，提出的系统引入了多层感知器（MLP）模型，该模型对从附近基站收集的路径损失测量的统计特性进行培训，以决定GPS位置的真实性。实验结果表明，在我们提出的方法下检测GPS欺骗的准确性率超过93％，三个基站也可以达到80％，只有一个基站。
Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position. Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.
