基于归一化循环前缀相关谱的无人机识别技术

doi:10.19665/j.issn1001-2400.20230704

Abstract

Abstract:

Radio-frequency(RF)-based drone identification technology has the advantages of long detection distance and low environmental dependence,so that it has become an indispensable approach to monitoring drones.How to identify a drone effectively at the low signal-to-noise ratio(SNR) regime is a hot topic in current research.To ensure excellent video transmission quality,drones commonly adopt orthogonal frequency division multiplexing(OFDM) modulation with cyclic prefix(CP) as the modulation of video transmission links.Based on this property,we propose a drone identification algorithm based on the convolutional neural network(CNN) and normalized CP correlation spectrum.Specifically,we first analyze the OFDM symbol durations and CP durations of drone signals,on the basis of which the normalized CP correlation spectrum is calculated.When the modulation parameters of a drone signal match the calculated normalized CP correlation spectrum,several correlation peaks will appear in the normalized CP correlation spectrum.The positions of these peaks reflect the protocol characteristics of drone signals,such as frame structure and burst rules.Finally,for identifying drones,a CNN is trained to extract these characteristics from the normalized CP correlation spectrum.In this work,a universal software radio peripheral(USRP) X310 is utilized to collect the RF signals of five drones to construct the experimental dataset.Experimental results show that the proposed algorithm performs better than spectrum-based and spectrogram-based algorithms,and it remains effective at low SNRs.

Key words: drones, RF signal, OFDM, CP correlation spectrum

CLC Number:

TN911.7

ZHANG Hanshuo, LI Tao, LI Yongzhao, WEN Zhijin. Drone identification based on the normalized cyclic prefix correlation spectrum[J].Journal of Xidian University, 2024, 51(2): 68-75.

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References 27

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无人机	突发时长/ms	突发间隔/ms	符号个数	T_s/μs	T_c/μs
DJI Mavic Air	0.2~1.5	0.3~18	7~57	12.8	3.2
DJI Mavic Air 2	2~5	1~2	29~71	66.67	4.68
DJI Inspire 2	9.79	4.21	95	91.429	11.428
Autel EVO Lite	0.86	0.14~7.15	12	66.67	4.68
FIMI X8SE	0.18~1.4	0.05~4	9~170	6.4	1.6

Drone identification based on the normalized cyclic prefix correlation spectrum

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