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

摘要/Abstract

摘要：

基于射频的无人机识别技术具有探测距离长、环境依赖性低的优点,已成为无人机监控系统不可或缺的技术手段。如何在低信噪比条件下有效识别无人机是当前热点问题。为保证良好的图传质量,无人机通常采用带有循环前缀结构的正交频分复用(OFDM)调制作为图传链路的调制方式。据此特性,首先提出一种基于归一化循环前缀相关谱和卷积神经网络的无人机识别算法。依据对无人机信号的OFDM符号周期和循环前缀长度的分析结果,计算信号归一化循环前缀相关谱。当归一化循环前缀相关谱的计算参数与无人机信号的调制参数匹配时,谱线中会出现若干相关峰,峰的位置分布反映了无人机信号帧结构、突发规则等协议特征。然后,利用卷积神经网络对归一化循环前缀相关谱进行特征分析和提取,从而识别无人机。最后,利用通用软件无线电平台USRP X310对5款无人机的射频信号进行采集,构建实验数据集。实验结果表明,该算法优于基于频谱和基于时频谱的算法,且在低信噪比下仍然有效。

关键词: 无人机, 射频信号, OFDM, 归一化循环前缀相关谱

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

中图分类号:

TN911.7

张涵硕, 李涛, 李勇朝, 温志津. 基于归一化循环前缀相关谱的无人机识别技术[J]. 西安电子科技大学学报, 2024, 51(2): 68-75.

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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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