DroneRFb-DIR:用于非合作无人机个体识别的射频信号数据集  

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

作　　者：任俊宇 俞宁宁 周成伟 史治国[1,4] 陈积明[2,3] REN Junyu;YU Ningning;ZHOU Chengwei;SHI Zhiguo;CHEN Jiming(College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310027,China;State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China;School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China;Jinhua Institute of Zhejiang University,Jinhua 321037,China)

机构地区：[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

出　　处：《电子与信息学报》2025年第3期573-581,共9页Journal of Electronics & Information Technology

基　　金：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)。

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。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-ofsight).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 ach

关 键 词：无人机个体识别 频谱感知 非合作无人机 射频检测数据集 

分 类 号：TN975[电子电信—信号与信息处理]