成都理工大学计算机与网络安全学院;信息系统安全技术重点实验室;
近年来,无人机在各个领域得到广泛应用,但越来越多的无人机也带来了一系列隐私和安全问题。因此,对无人机的检测和管理变得至关重要,目前通过射频指纹识别无人机的方法取得了较大进展。在结合国内外大量研究成果的基础上,对无人机射频指纹系统的各个步骤进行深入探讨。分析了各种无人机识别技术的优缺点,并详细阐述了无人机通信方式和射频指纹的形成机理与特性。根据信号收集、预处理、特征提取和分类识别4个阶段,对无人机射频指纹系统的相关技术进行了系统梳理,讨论了无人机射频指纹系统性能评估方法。对当前无人机射频指纹研究现状进行了问题分析与展望,包括射频指纹数据集缺乏、射频指纹稳定性、射频指纹的唯一性以及深度学习在射频指纹应用领域的探索,并进行了全文总结。
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下载次数 | 被引频次 | 阅读次数 |
[1] HUYNH-THE T,PHAM Q V,NGUYEN T V,et al.RF-UAVNet:High-performance Convolutional Network for RF-based Drone Surveillance Systems[J].IEEE Access,2022,10:49696-49707.
[2] INANI K N,SANGWAN K S,DHIRA J.Machine Learning Based Framework for Drone Detection and Identification Using RF Signals[C]//2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT).Kottayam:IEEE,2023:1-8.
[3] MEDAIYESE O O,SYED A,LAUF A P.Machine Learning Framework for RF-based Drone Detection and Identification System[C]//2021 2nd International Conference on Smart Cities,Automation & Intelligent Computing Systems (ICON-SONICS).Tangerang:IEEE,2021:58-64.
[4] LIEW C F,YAIRI T.Companion Unmanned Aerial Vehicles:A Survey[EB/OL].(2020-01-14)[2023-06-22].https://arxiv.org/abs/2001.04637.
[5] CHEN Y F,ZHU L,YAO C H,et al.Global Context-based Threshold Strategy for Drone Identification Under the Low SNR Condition[J].IEEE Internet of Things Journal,2023,10(2):1332-1346.
[6] JIAN M,LU Z Z,CHEN V C.Drone Detection and Tracking Based on Phase-interferometric Doppler Radar[C]//2018 IEEE Radar Conference (RadarConf18).Oklahoma City:IEEE,2018:1146-1149.
[7] TIWARI R,DUBEY A K.Detection of Camouflaged Drones Using Computer Vision and Deep Learning Techniques[C]//2022 12th International Conference on Cloud Computing,Data Science & Engineering (Confluence).Noida:IEEE,2022:380-383.
[8] JEON S,SHIN J W,LEE Y J,et al.Empirical Study of Drone Sound Detection in Real-life Environment with Deep Neural Networks[C]//2017 25th European Signal Processing Conference (EUSIPCO).Kos:IEEE,2017:1858-1862.
[9] GUVENC I,KOOHIFAR F,SINGH S,et al.Detection,Tracking,and Interdiction for Amateur Drones[J].IEEE Communications Magazine,2018,56(4):75-81.
[10] SADOVSKIS J,ABOLTINS A.Modern Methods for UAV Detection,Classification,and Tracking[C]//2022 IEEE 63th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON).Riga:IEEE,2022:1-7.
[11] JURN Y N,MAHMOOD S A,ALDHAIBANI J A.Anti-drone System Based Different Technologies:Architecture,Threats and Challenges[C]//2021 11th IEEE International Conference on Control System,Computing and Engineering (ICCSCE).Penang:IEEE,2021:114-119.
[12] CAI Z R,LIU Z Y,KOU L.Reliable UAV Monitoring System Using Deep Learning Approaches[J].IEEE Transactions on Reliability,2022,71(2):973-983.
[13] 陈翔,汪连栋,许雄,等.基于Raw I/Q和深度学习的射频指纹识别方法综述[J].雷达学报,2023,12(1):214-234.
[14] 曾勇虎,陈翔,林云,等.射频指纹识别的研究现状及趋势[J].电波科学学报,2020,35(3):305-315.
[15] 张振,贾济铖,康健,等.射频指纹识别技术方法综述[J].无线电通信技术,2021,47(3):249-258.
[16] 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.
[17] SOLTANIEH N,NOROUZI Y,YANG Y,et al.A Review of Radio Frequency Fingerprinting Techniques[J].IEEE Journal of Radio Frequency Identification,2020,4(3):222-233.
[18] BASAK S,RAJENDRAN S,POLLIN S,et al.Drone Classification from RF Fingerprints Using Deep Residual Nets[C]//2021 International Conference on COMmunication Systems & NETworkS (COMSNETS).Bangalore:IEEE,2021:548-555.
[19] ZUO M,XIE S G,ZHANG X,et al.Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference[J].IEEE Access,2021,9:88844-88851.
[20] XU C T,CHEN B W,LIU Y X,et al.RF Fingerprint Measurement for Detecting Multiple Amateur Drones Based on STFT and Feature Reduction[C]//2020 Integrated Communications Navigation and Surveillance Conference (ICNS).Herndon:IEEE,2020:4G1-1-4G1-7.
[21] GU J,SOLTANI N,NADERI M Y,et al.It’s a Bird,It’s a Plane,It’s “That” UAV:RF Fingerprinting During Flight[C]//2021 55th Asilomar Conference on Signals,Systems,and Computers.Pacific Grove:IEEE,2021:300-304.
[22] NIE W,HAN Z C,ZHOU M,et al.UAV Detection and Identification Based on WiFi Signal and RF Fingerprint[J].IEEE Sensors Journal,2021,21(12):13540-13550.
[23] NIE W,HAN Z C,LI Y,et al.UAV Detection and Localization Based on Multi-dimensional Signal Features[J].IEEE Sensors Journal,2022,22(6):5150-5162.
[24] MEDAIYESE O O,EZUMA M,LAUF A P,et al.Hierarchical Learning Framework for UAV Detection and Identification[J].IEEE Journal of Radio Frequency Identification,2022,6:176-188.
[25] SOLTANI N,REUS-MUNS G,SALEHI B,et al.RF Fingerprinting Unmanned Aerial Vehicles with Non-standard Transmitter Waveforms[J].IEEE Transactions on Vehicular Technology,2020,69(12):15518-15531.
[26] 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.
[27] EZUMA M,ERDEN F,ANJINAPPA C K,et al.Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques[C]//2019 IEEE Aerospace Conference.Big Sky:IEEE,2019:1-13.
[28] MEDAIYESE O O,EZUMA M,LAUF A P,et al.Semi-supervised Learning Framework for UAV Detection[C]//2021 IEEE 32nd Annual International Symposium on Personal,Indoor and Mobile Radio Communications (PIMRC).Helsinki:IEEE,2021:1185-1190.
[29] BREMNES K,MOEN R,YEDURI S R,et al.Classification of UAVs Utilizing Fixed Boundary Empirical Wavelet Sub-bands of RF Fingerprints and Deep Convolutional Neural Network[J].IEEE Sensors Journal,2022,22(21):21248-21256.
[30] GE C H,YANG S B,SUN W J,et al.For RF Signal-based UAV States Recognition,Is Pre-processing Still Important at the Era of Deep Learning?[C]//2021 7th International Conference on Computer and Communications (ICCC).Chengdu:IEEE,2021:2292-2296.
[31] ZHANG J B,WANG Q W,GUO X C,et al.Radio Frequency Fingerprint Identification Based on Logarithmic Power Cosine Spectrum[J].IEEE Access,2022,10:79165-79179.
[32] XU C T,HE F Y,CHEN B W,et al.Adaptive RF Fingerprint Decomposition in Micro UAV Detection Based on Machine Learning[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Toronto:IEEE,2021:7968-7972.
[33] LI T T,HONG Z,CAI Q M,et al.BisSiam:Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(12):12109-12124.
[34] LI C Q,WANG J M,WANG W Y,et al.RF-based on Feature Fusion and Convolutional Neural Network Classification of UAVs[C]//2022 IEEE 8th International Conference on Computer and Communications (ICCC).Chengdu:IEEE,2022:1899-1904.
[35] LIANG H T,WANG R T,XU M,et al.Few-shot Learning UAV Recognition Methods Based on the Tri-residual Semantic Network[J].IEEE Communications Letters,2022,26(9):2072-2076.
[36] JAFARI H,BLASCH E,PHAM K,et al.Signature-aware RF Exploitation(SNARE) Fingerprinting Using Deep Learning to Identify UAVs[C]//2022 IEEE Aerospace Conference (AERO).Big Sky:IEEE,2022:1-12.
[37] ZHAO C D,SHI M X,CAI Z B,et al.Detection of Unmanned Aerial Vehicle Signal Based on Gaussian Mixture Model[C]//2017 12th International Conference on Computer Science and Education (ICCSE).Houston:IEEE,2017:289-293.
[38] MOHANTI S,SOLTANI N,SANKHE K,et al.AirID:Injecting a Custom RF Fingerprint for Enhanced UAV Identification Using Deep Learning[C]//GLOBECOM 2020-2020 IEEE Global Communications Conference.Taipei:IEEE,2020:1-6.
[39] ZHAO C D,CHEN C Y,CAI Z B,et al.Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs[C]//2018 IEEE Global Communications Conference (GLOBECOM).Abu Dhabi:IEEE,2018:206-212.
[40] YAKKATI R R,GADE A,KODURUB H,et al.Classification of UAVs Using Time-frequency Analysis of Remote Control Signals and CNN[C]//2022 IEEE International Symposium on Smart Electronic Systems (iSES).Warangal:IEEE,2022:1-6.
[41] RAJENDRAN S,SUN Z,LIN F,et al.Injecting Reliable Radio Frequency Fingerprints Using Metasurface for the Internet of Things[J].IEEE Transactions on Information Forensics and Security,2021,16:1896-1911.
基本信息:
DOI:
中图分类号:TP391.44;V279
引用信息:
[1]王豪,罗俊松,王惠明等.无人机射频指纹识别方法综述[J].无线电工程,2024,54(11):2672-2684.
基金信息:
四川省国际科技创新合作/港澳台科技创新合作项目(2023YFH0092); 四川省区域创新合作项目(2022YFQ0017); 国家自然科学基金(62172060)~~