DroneRFb-DIR: 用于非合作无人机个体识别的射频信号数据集

任俊宇; 俞宁宁; 周成伟; 史治国; 陈积明

doi:10.11999/JEIT240804

DroneRFb-DIR: 用于非合作无人机个体识别的射频信号数据集

doi: 10.11999/JEIT240804

任俊宇^{1, 2},
俞宁宁^{1, 2},
周成伟^{1, 2},
史治国^{1, 4, ,},
陈积明^{2, 3}

1.
浙江大学信息与电子工程学院杭州 310027
2.
浙江大学工业控制技术全国重点实验室杭州 310027
3.
杭州电子科技大学自动化学院杭州 310018
4.
浙江大学金华研究院金华 321037

基金项目: 国家自然科学基金(U21A20456, 62271444)，中央高校基本科研业务费(226-2023-00111, 226-2024-00004)

详细信息

作者简介:
任俊宇：男，博士生，研究方向为反无人机检测识别、信号估计等

俞宁宁：男，博士生，研究方向为反无人机检测、电磁频谱认知、信号识别等

周成伟：男，博士，研究员，研究方向为阵列信号处理、张量信号处理、无人机智能监测技术等

史治国：男，博士，教授，研究方向为信号处理及其定位应用、物联网等

陈积明：男，博士，教授，研究方向为网络优化与控制、网络系统安全、工业大数据与物联网等

通讯作者:
史治国　shizg@zju.edu.cn

中图分类号: TN975
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- 被引次数: 0
出版历程
- 收稿日期: 2024-09-19
- 修回日期: 2025-02-21
- 网络出版日期: 2025-02-26
- 刊出日期: 2025-03-01

DroneRFb-DIR: An RF Signal Dataset for Non-cooperative Drone Individual Identification

REN Junyu^{1, 2},
YU Ningning^{1, 2},
ZHOU Chengwei^{1, 2},
SHI Zhiguo^{1, 4
, ,},
CHEN Jiming^{2, 3}

1.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
2.
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
3.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
4.
Jinhua Institute of Zhejiang University, Jinhua 321037, China

Funds: The National Natural Science Foundation of China (U21A20456, 62271444), The Fundamental Research Funds for Central Universities (226-2023-00111, 226-2024-00004)

摘要

摘要: 无人机射频检测是实现非合作无人机管控的手段之一，而基于射频信号的无人机个体识别(DIR)是无人机检测的重要环节。鉴于当前DIR开源数据集缺失，该文公开了一个名为DroneRFb-DIR的无人机射频信号数据集。该数据集使用软件无线电设备采集无人机与遥控器间通信的射频信号，包含城市场景下的无人机种类共6类(每类无人机各包含3架不同个体)以及1类背景参考信号。采样信号存储为最原始的I/Q数据，每类数据包含不少于40个片段，每个片段包含不少于4 M个采样点。信号采集范围为2.4～2.48 GHz，包含无人机飞控信号、图传信号以及周围干扰设备的信号。该数据集包含详细的个体编号和视距或非视距场景标注，并已划分训练集与测试集，以便于用户进行识别算法验证和性能对比分析。与此同时，该文提供了一种基于快速频率估计和时域相关分析的无人机个体识别方法，并在该数据集上验证了所提方法的有效性。
- 无人机个体识别 /
- 频谱感知 /
- 非合作无人机 /
- 射频检测数据集
Abstract: RF-based drone detection is an essential method for managing non-cooperative drones, with Drone Individual Recognition (DIR) via RF signals being a key component in the detection process. Given the current scarcity of DIR datasets, this paper proposes an open-source DroneRFb-DIR dataset for RF-based DIR. The dataset is constructed by capturing RF signals exchanged between drones and their remote controllers using a Software-Defined Radio (SDR). It includes signals from six types of drones, each with three different individuals, as well as background signals from urban environments. The captured signals are stored in raw I/Q format, and each drone type consists of over 40 signal segments, with each segment containing more than 4 million sample points. The RF sampling range spans from 2.4 GHz to 2.48 GHz, covering Flight Control Signals (FCS), Video Transmission Signals (VTS), and interference from surrounding devices. The dataset is annotated with entity identifiers (e.g., drone type and individual) and environmental labels (line-of-sight vs. non-line-of-sight). A DIR method based on fast frequency estimation and time-domain correlation analysis is also proposed and validated using this dataset. Objective: Drones are increasingly used in sectors such as geospatial mapping, aerial photography, traffic monitoring, and disaster relief, playing a significant role in modern industries and daily life. However, the rise in unauthorized drone operations presents serious threats to national security, public safety, and privacy, especially in urban areas. While existing methods emphasize general drone detection and classification, they struggle to distinguish individual drones of the same type, which is crucial for distinguishing friend from foe, analyzing swarm dynamics, and implementing effective countermeasures. This study addresses this gap by introducing the DroneRFb-DIR dataset, a large-scale, open-source RF signal dataset for non-cooperative DIR. Additionally, a novel method based on fast frequency estimation and time-domain correlation analysis is proposed to achieve accurate drone identification in urban environments. Methods: The DroneRFb-DIR dataset is developed using SDR device to capture RF signals in an urban environment with interference from devices like Wi-Fi and Bluetooth. It includes signals from six drone types, each with three individual units, as well as background reference signals. The dataset is collected at an 80 MHz sampling rate in the 2.4～2.48 GHz band and stored in raw I/Q format for detailed analysis. Each signal is annotated with identifiers (e.g., drone type and individual) and scene labels (line-of-sight and non-line-of-sight). For algorithm validation, the dataset is partitioned into training and testing sets. The proposed method consists of three key stages: (1) Signal Detection: A dynamic bandpass or band-stop filter isolates drone control signals from background noise and interference. (2) Frequency Localization: Adaptive filtering and frequency estimation to identify the spectral location of drone signals. (3) Identity Feature Extraction: Correlation analysis extracts identity features from control signal segments to differentiate individual drones, focusing on unique frequency modulation patterns. Results and Discussions: The dataset comprises 4,690 signal segments, each containing with over 4 million sample points. Experiments demonstrated the effectiveness of the proposed method (Table 3), showing high rejection rates of background signals and accurate identification of specific drone types. However, performance varied across drone types due to factors such as signal quality, environmental interference, and control signal characteristics. For instance, drones with low-SNR signals or less distinct frequency modulation patterns posed greater challenges for identification. Despite these difficulties, the method achieved competitive accuracy in identifying individual drones, even in non-line-of-sight conditions. These findings underscore the importance of advanced filtering and feature extraction for robust DIR in complex urban environments. Conclusions: This study addresses the critical need for DIR technologies by introducing the DroneRFb-DIR dataset and a novel identification method. Featuring six drone types, 18 individual drones, and one background signal class, the dataset is the first large-scale open-source resource for non-cooperative DIR in urban scenarios (Table 2). The proposed method effectively separates drone signals from interference and accurately identifies individual drones. Future work will focus on expanding the dataset with more diverse drone types, additional environmental scenarios (e.g., multipath interference and dynamic drone states), and machine learning models for improved recognition. Optimization of non-learning methods will also be explored to enhance feature extraction and identification rates, especially for drones with weaker signal characteristics.
- Drone individual Recognition (DIR) /
- Spectrum sensing /
- Non-cooperative drone /
- RF detection dataset

HTML全文

图 1 数据采集场景

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图 2 DJI Mavic 3 Pro无人机信号时频图

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图 3 无人机图传(红色实线框)与飞控信号(蓝色实线框)特征示意

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图 4 宽带信号的检出流程

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图 5 DJI Mavic 3 Pro的无人机飞控信号时频图

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图 6 相邻两个时间窗FFT谱图

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图 7 无人机飞控信号组成

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图 8 无人机个体飞控信号相关性曲线

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表 1 无人机探测手段特点

探测手段	最大有效距离(m)	原理	缺点
雷达	8000	微多普勒	无人机雷达截面积小，成本高，不适合城市场景
音频	200	时频特征	覆盖范围小，受噪声影响大
视觉	1500	外观特征和运动特征	受遮挡、天气环境影响大
射频	5000	通信信道	易受城市环境下干扰信号影响

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表 2 个体标签与型号对应关系

个体标签	型号
A1, A2, A3	DJI Mavic 3 Pro
B	背景
C1, C2, C3	DJI Mini 2 SE
D1, D2, D3	DJI Mini 4 Pro
E1, E2, E3	DJI Mini 3
F1, F2, F3	DJI Air 3
G1, G2, G3	DJI Air 2S

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表 3 无人机个体识别结果

种类标签	识别率(%)
A	63.96
B	100.00
C	60.74
D	29.63
E	68.62
F	37.50
G	67.95

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