Allahham Mhd Saria, Al-Sa'd Mohammad F, Al-Ali Abdulla, Mohamed Amr, Khattab Tamer, Erbad Aiman
Qatar University, Department of Computer Science and Engineering, Doha, Qatar.
Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland.
Data Brief. 2019 Aug 26;26:104313. doi: 10.1016/j.dib.2019.104313. eCollection 2019 Oct.
Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article "RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database" (Al-Sa'd et al., 2019).
现代技术已将我们带入信息时代，使得生成和记录大量新数据变得更加容易。数据集有助于分析情况，以便更好地理解，更重要的是有助于决策。因此，数据集及其可用于的用途已成为越来越有价值的商品。本文介绍了DroneRF数据集：一个基于射频（RF）的数据集，包含处于不同模式（包括关闭、开启和连接、悬停、飞行以及视频录制）下运行的无人机。该数据集包含RF活动的记录，由从3架不同无人机收集的227个记录片段组成，以及没有无人机时的背景RF活动记录。这些数据是通过RF接收器收集的，这些接收器拦截无人机与飞行控制模块的通信。接收器通过PCIe电缆连接到两台笔记本电脑，这两台笔记本电脑运行一个程序，负责在数据库中获取、处理和存储感测到的RF数据。关于如何解释和使用这个数据集的示例可以在相关研究文章《使用深度学习方法进行基于RF的无人机检测和识别：迈向大型开源无人机数据库的倡议》（Al-Sa'd等人，2019年）中找到。
