DroneRFa丨A Large-scale Dataset of Drone Radio Frequency Signals for Detecting Low-altitude Drones

Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: REN Junyu, YU Ningning, ZHOU Chengwei, SHI Zhiguo, CHEN Jiming. DroneRFb-DIR: An RF Signal Dataset for Non-cooperative Drone Individual Identification[J]. Journal of Electronics & Information Technology, 2025, 47(3): 573-581. doi: 10.11999/JEIT240804 Authors Unit: Ren Junyu, Yu Ningning, Zhou Chengwei, Shi Zhiguo, Chen JimingAuthor:(1) Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Zhejiang University(2) College of Control Science and Engineering, Zhejiang University(3) Chengde City’s Police Department of Hebei ProvinceCorrespondent: SHI Zhiguo，[e-mail] link：DroneRFa：用于侦测低空无人机的大规模无人机射频信号数据集Funds: The National Natural Science Foundation of China (U21A20456, 62271444, 61901413), The Zhejiang Provincial Natural Science Foundation of China (LZ23F010007), Zhejiang University Education Foundation Qizhen Scholar Foundation, 5G Open Laboratory of Hangzhou Future Sci-Tech City, The Fundamental Research Funds for the Central Universities (226-2022-00107)Abstract: A large-scale dataset of drone radio frequency signals, namely DroneRFa, is constructed to research and develop anti-drone detection and recognition technologies. This dataset uses a software-defined radio device to monitor communication signals between drones and their controllers, including 9 types of flying drone signals in an outdoor environment, 15 types of drone signals in an indoor environment, and 1 type of background signal as a reference. Each type of data has no less than 12 segments, each containing more than 100 million sampling points. The data acquisition covered three Industrial Scientific Medical (ISM) radio bands, and recorded the multifrequency communication activity of drones. The dataset has detailed flying distance and communication frequency band labeling, which are represented with prefix characters and binary codes to facilitate easy access to specific data required by users. Furthermore, this paper proposes two drone identification schemes based on spectral and visual statistical features and deep learning representation to verify the reliability and validity of the dataset.

DroneRFa：面向低空无人机检测的大规模无人机射频（Radio Frequency, RF）信号数据集
于宁宁¹, 毛盛健¹, 周成伟¹, 孙国伟³, 史治国¹, 陈积明¹,²
¹浙江省协同感知与自主无人系统重点实验室（浙江大学），杭州 310027，中国
²浙江大学控制科学与工程学院，杭州 310027，中国
³河北省承德市公安局，承德 067000，中国

为研发反无人机检测与识别技术，本研究构建了大规模无人机射频信号数据集DroneRFa。本数据集采用软件定义无线电（Software-Defined Radio, SDR）设备，采集无人机与其遥控器之间的通信信号，包含室外环境下9类飞行无人机信号、室内环境下15类无人机信号，以及1类背景信号作为参照。每类数据包含不少于12段采样数据，每段采样数据的采样点数均超过1亿。数据采集覆盖三个工业科学医疗（Industrial Scientific Medical, ISM）无线电频段，记录了无人机的多频通信活动。该数据集附带详细的飞行距离与通信频段标注，采用前缀字符与二进制编码进行组织，便于用户快速获取所需的特定数据。此外，本文提出了两种基于频谱与视觉统计特征、结合深度学习表征的无人机识别方案，以验证该数据集的可靠性与有效性。

引用格式：于宁宁, 毛盛健, 周成伟, 孙国伟, 史治国, 陈积明. DroneRFa：面向低空无人机检测的大规模无人机射频信号数据集[J]. 电子与信息学报, 2024, 46(4): 1147-1156. doi: 10.11999/JEIT230570 URL：https://jeit.ac.cn/cn/article/doi/10.11999/JEIT230570

搜集汇总

背景与挑战

背景概述

DroneRFa是一个大规模无人机射频信号数据集，旨在支持低空无人机检测技术的研究与开发。它包含室外9种和室内15种无人机信号以及背景信号，每个信号类型具有大量采样点，覆盖多个ISM频段，并提供了详细的飞行距离和频段标签以方便使用。该数据集通过基于频谱、视觉特征和深度学习的识别方案验证了其可靠性和有效性。