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.郑重声明：使用本数据集发表论文、著作等各类学术作品时，必须正式引用本数据集所属的学术论文： 引用：任俊宇、于宁宁、周成伟、史治国、陈济民。DroneRFb-DIR：一种用于非合作无人机个体识别的射频信号数据集[J]. 《电子与信息学报》, 2025, 47(3): 573-581. doi: 10.11999/JEIT240804 作者单位：任俊宇、于宁宁、周成伟、史治国、陈济民(1) 浙江大学浙江省协同感知与自主无人系统重点实验室；(2) 浙江大学控制科学与工程学院；(3) 河北省承德市公安局 通讯作者：史治国，[e-mail] 原始链接：DroneRFa：用于侦测低空无人机的大规模无人机射频信号数据集 资助项目：国家自然科学基金（U21A20456、62271444、61901413）、浙江省自然科学基金（LZ23F010007）、浙江大学教育基金会启真学者基金、杭州未来科技城5G开放实验室、中央高校基本科研业务费专项资金（226-2022-00107） 摘要：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）设备采集无人机与其控制器之间的通信信号，涵盖室外环境下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段数据，每段数据的采样点数均超过1亿。数据采集覆盖了三个工业科学医疗（Industrial Scientific Medical, ISM）无线电频段，并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，通过前缀字符与二进制编码进行表示，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、视觉统计特征以及深度学习表征的无人机识别方案，以验证本数据集的可靠性与有效性。

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，用于低空无人机检测与识别研究。它包含室外9种、室内15种无人机信号及1种背景信号，每类数据至少12段，每段超过1亿采样点，覆盖三个ISM频段，并带有飞行距离和通信频段的详细标注。