DOI: 10.57760/sciencedb.18475
title: DroneRFa丨A Large-scale Dataset of Drone Radio Frequency Signals for Detecting Low-altitude Drones
title: DroneRFa丨用于侦测低空无人机的大规模无人机射频信号数据集

description: 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，shizg@zju.edu.cnOriginal 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.
description: 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，shizg@zju.edu.cnOriginal 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.