DOI: 10.57760/sciencedb.20454
title: DroneRFb-DIR: An RF Signal Dataset for Non-cooperative Drone Individual Identification
title: DroneRFb-DIR: 用于非合作无人机个体识别的射频信号数据集

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: Ren Junyu, Yu Ningning, Zhou Chengwei, Shi Zhiguo, Chen JimingAuthor: College of Information Science and Electronic Engineering, Zhejiang UniversityState Key Laboratory of Industrial Control Technology, Zhejiang UniversitySchool of Automation, Hangzhou Dianzi UniversityJinhua Institute of Zhejiang UniversityCorrespondent: SHI Zhiguo，shizg@zju.edu.cnOriginal link：DroneRFb-DIR: 用于非合作无人机个体识别的射频信号数据集Funds: The National Natural Science Foundation of China (U21A20456, 62271444), The Fundamental Research Funds for Central Universities (226-2023-00111, 226-2024-00004)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.
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: Ren Junyu, Yu Ningning, Zhou Chengwei, Shi Zhiguo, Chen JimingAuthor: College of Information Science and Electronic Engineering, Zhejiang UniversityState Key Laboratory of Industrial Control Technology, Zhejiang UniversitySchool of Automation, Hangzhou Dianzi UniversityJinhua Institute of Zhejiang UniversityCorrespondent: SHI Zhiguo，shizg@zju.edu.cnOriginal link：DroneRFb-DIR: 用于非合作无人机个体识别的射频信号数据集Funds: The National Natural Science Foundation of China (U21A20456, 62271444), The Fundamental Research Funds for Central Universities (226-2023-00111, 226-2024-00004)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.