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

YU Ningning; MAO Shengjian; ZHOU Chengwei; SUN Guowei; SHI Zhiguo; CHEN Jiming

doi:10.11999/JEIT230570

Volume 46 Issue 4

Apr. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1147-1156

YU Ningning, MAO Shengjian, ZHOU Chengwei, SUN Guowei, SHI Zhiguo, CHEN Jiming. DroneRFa: A Large-scale Dataset of Drone Radio Frequency Signals for Detecting Low-altitude Drones[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1147-1156. doi: 10.11999/JEIT230570

Citation:

YU Ningning, MAO Shengjian, ZHOU Chengwei, SUN Guowei, SHI Zhiguo, CHEN Jiming. DroneRFa: A Large-scale Dataset of Drone Radio Frequency Signals for Detecting Low-altitude Drones[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1147-1156. doi: 10.11999/JEIT230570

Citation:

YU Ningning, MAO Shengjian, ZHOU Chengwei, SUN Guowei, SHI Zhiguo, CHEN Jiming. DroneRFa: A Large-scale Dataset of Drone Radio Frequency Signals for Detecting Low-altitude Drones[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1147-1156. doi: 10.11999/JEIT230570

PDF( 6648 KB)

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

doi: 10.11999/JEIT230570

1.
Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
2.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
3.
Chengde City’s Police Department of Hebei Province, Chengde 067000, China

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)

Received Date: 2023-06-08
Rev Recd Date: 2023-07-24

Available Online: 2023-07-27

Publish Date: 2024-04-24

Abstract

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.
- Artificial intelligence,
- Anti-drone detection,
- Spectrum learning,
- Signal identification

FullText(HTML)

References(23)

References

[1]	BIKOV T, MIHAYLOV G, ILIEV T, et al. Drone surveillance in the modern agriculture[C]. Proceedings of the 8th International Conference on Energy Efficiency and Agricultural Engineering, Ruse, Bulgaria, 2022: 1–4.
[2]	QUBAA A R, THANNOUN R G, and MOHAMMED R M. UAVs/drones for photogrammetry and remote sensing: Nineveh archaeological region as a case study[J]. World Journal of Advanced Research and Reviews, 2022, 14(3): 358–368. doi: 10.30574/wjarr.2022.14.3.0539.
[3]	QU Chengyi, SORBELLI F B, SINGH R, et al. Environmentally-aware and energy-efficient multi-drone coordination and networking for disaster response[J]. IEEE Transactions on Network and Service Management, 2023, 20(2): 1093–1109. doi: 10.1109/TNSM.2023.3243543.
[4]	Team DRONEII. com. Global drone market report 2022–2030[EB/OL]. https://droneii.com/product/drone-market-report, 2023.
[5]	Dedrone. Worldwide drone incidents[EB/OL]. https://www.dedrone.com/resources/incidents-new/all, 2023.
[6]	宋晨, 周良将, 吴一戎, 等. 基于自相关-倒谱联合分析的无人机旋翼转动频率估计方法[J]. 电子与信息学报, 2019, 41(2): 255–261. doi: 10.11999/JEIT180399. SONG Chen, ZHOU Liangjiang, WU Yirong, et al. An estimation method of rotation frequency of unmanned aerial vehicle based on auto-correlation and cepstrum[J]. Journal of Electronics &Information Technology, 2019, 41(2): 255–261. doi: 10.11999/JEIT180399.
[7]	杨勇, 王雪松, 张斌. 基于时频检测与极化匹配的雷达无人机检测方法[J]. 电子与信息学报, 2021, 43(3): 509–515. doi: 10.11999/JEIT200768. YANG Yong, WANG Xuesong, and ZHANG Bin. Radar detection of unmanned aerial vehicles based on time-frequency detection and polarization matching[J]. Journal of Electronics &Information Technology, 2021, 43(3): 509–515. doi: 10.11999/JEIT200768.
[8]	张霞, 余道杰, 刘广怡, 等. 一种无人机蜂群飞行同步检测和抑制方案[J]. 电子与信息学报, 2023, 45(12): 4317–4326. ZHANG Xia, YU Daojie, LIU Guangyi, et al. Countermeasures against UAV swarm through detection and suppression of fly synchronization[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4317–4326.
[9]	SHI Xiufang, YANG Chaoqun, XIE Weige, et al. Anti-drone system with multiple surveillance technologies: Architecture, implementation, and challenges[J]. IEEE Communications Magazine, 2018, 56(4): 68–74. doi: 10.1109/MCOM.2018.1700430.
[10]	SHI Zhiguo, CHANG Xianyu, YANG Chaoqun, et al. An acoustic-based surveillance system for amateur drones detection and localization[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3): 2731–2739. doi: 10.1109/TVT.2020.2964110.
[11]	ZHANG Zongyu, SHI Zhiguo, and GU Yujie. Ziv-Zakai bound for DOAs estimation[J]. IEEE Transactions on Signal Processing, 2023, 71: 136–149. doi: 10.1109/TSP.2022.3229946.
[12]	GUVENC I, KOOHIFAR F, SINGH S, et al. Detection, tracking, and interdiction for amateur drones[J]. IEEE Communications Magazine, 2018, 56(4): 75–81. doi: 10.1109/MCOM.2018.1700455.
[13]	KHAN M A, MENOUAR H, ELDEEB A, et al. On the detection of unauthorized drones-techniques and future perspectives: A review[J]. IEEE Sensors Journal, 2022, 22(12): 11439–11455. doi: 10.1109/JSEN.2022.3171293.
[14]	ALLAHHAM M H D S, AL-SA'D M F, AL-ALI A, et al. DroneRF dataset: A dataset of drones for RF-based detection, classification and identification[J]. Data in Brief, 2019, 26: 104313. doi: 10.1016/j.dib.2019.104313.
[15]	BISIO I, GARIBOTTO C, LAVAGETTO F, et al. Blind detection: Advanced techniques for WiFi-based drone surveillance[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 938–946. doi: 10.1109/TVT.2018.2884767.
[16]	XIE Yuelei, JIANG Ping, GU Yi, et al. Dual-source detection and identification system based on UAV radio frequency signal[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2006215. doi: 10.1109/TIM.2021.3103565.
[17]	EZUMA M, ERDEN F, ANJINAPPA C K, et al. Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference[J]. IEEE Open Journal of the Communications Society, 2020, 1: 60–76. doi: 10.1109/OJCOMS.2019.2955889.
[18]	FU Hua, ABEYWICKRAMA S, ZHANG Lihao, et al. Low-complexity portable passive drone surveillance via SDR-based signal processing[J]. IEEE Communications Magazine, 2018, 56(4): 112–118. doi: 10.1109/MCOM.2018.1700424.
[19]	ALSOLIMAN A, RIGONI G, LEVORATO M, et al. COTS drone detection using video streaming characteristics[C]. Proceedings of the 22nd International Conference on Distributed Computing and Networking, Nara, Japan, 2021: 166–175.
[20]	ALIPOUR-FANID A, DABAGHCHIAN M, WANG Ning, et al. Machine learning-based delay-aware UAV detection and operation mode identification over encrypted Wi-Fi traffic[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 2346–2360. doi: 10.1109/TIFS.2019.2959899.
[21]	AL-SA’D M F, AL-ALI A, MOHAMED A, et al. RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database[J]. Future Generation Computer Systems, 2019, 100: 86–97. doi: 10.1016/j.future.2019.05.007.
[22]	BASAK S, RAJENDRAN S, POLLIN S, et al. Combined RF-based drone detection and classification[J]. IEEE Transactions on Cognitive Communications and Networking, 2022, 8(1): 111–120. doi: 10.1109/TCCN.2021.3099114.
[23]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.