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Data Brief. 2019 Aug 26;26:104313. doi: 10.1016/j.dib.2019.104313. eCollection 2019 Oct.

DroneRF dataset: A dataset of drones for RF-based detection, classification and identification.

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Qatar University, Department of Computer Science and Engineering, Doha, Qatar.
Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland.
Qatar University, Department of Electrical Engineering, Doha, Qatar.


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).


Anti-drone systems; Classification; Drone identification; UAV detection

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