论文标题
基于RF的无人机检测和识别系统的机器学习框架
Machine Learning Framework for RF-Based Drone Detection and Identification System
论文作者
论文摘要
无人机的出现为隐私和安全问题增加了新的维度。人们几乎没有或没有严格的法规可以购买或拥有无人机。因此，人们可以利用这些飞机侵入限制或私人区域。无人机检测和识别（DDI）系统是检测和识别无人机在区域中存在的方法之一。 DDI系统可以采用不同的传感技术，例如射频（RF）信号，视频，声音和热力，以检测入侵的无人机。在这项工作中，我们提出了一个基于机器的基于RF的DDI系统，该系统使用来自无人机到飞行控制器通信的低频段RF信号。我们使用XGBoost算法开发了三个机器学习模型，以检测并确定无人机的类型和无人机操作模式的存在。对于这三个XGBoost模型，我们使用10倍的交叉验证评估了模型，并获得了99.96％，90.73％和70.09％的平均准确度。
The emergence of drones has added new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a drone. For this reason, people can take advantage of these aircraft to intrude into restricted or private areas. A Drone Detection and Identification (DDI) system is one of the ways of detecting and identifying the presence of a drone in an area. DDI systems can employ different sensing technique such radio frequency (RF) signals, video, sounds and thermal for detecting an intruding drone. In this work, we propose a machine learning RF-based DDI system that uses low band RF signals from drone-to-flight controller communication. We develop three machine learning models using the XGBoost algorithm to detect and identify the presence of a drone, the type of drones and the operational mode of drones. For these three XGBoost models, we evaluated the models using 10-fold cross validation and we achieve average accuracy of 99.96%, 90.73% and 70.09% respectively.
