上海大学学报(自然科学版) ›› 2024, Vol. 30 ›› Issue (3): 435-450.doi: 10.12066/j.issn.1007-2861.2461

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基于图控分离和 2 次分类的无人机信号识别方法

王安平1, 吕振彬2, 梓轩1, 沈华明2, 黄家鹏2, 陆文斌2   

  1. 1. 上海大学 通信与信息工程学院, 上海 200444; 2. 上海航天电子通讯设备研究所 创新研究室, 上海 201109
  • 出版日期:2024-06-30 发布日期:2024-07-09
  • 通讯作者: 扆梓轩 (1991—), 男, 硕士生导师, 博士, 研究方向为天线、高功率微波和无线电侦测. E-mail:yizixuan@shu.edu.cn

UAV signal recognition method based on re-classification and separation of image transmission signal and flight control signal

WANG Anping1, LV Zhenbin2, YI Zixuan1, SHEN Huaming2, HUANG Jiapeng2, LU Wenbin2   

  1. 1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; 2. Innovation & Research Laboratory, Shanghai Spaceflight Electronic and Communication Equipment Research Institute, Shanghai 201109, China
  • Online:2024-06-30 Published:2024-07-09

摘要: 无人机 (unmanned aerial vehicle, UAV) 行业的快速发展给重要场所的低空空域带来安全隐患. 为了对无人机实施有效管制, 研制一套能够识别无人机信号的无线电侦测系统有重要意义. 针对相似无人机之间识别困难的问题, 提出了一种基于图控分离和 2 次分类的无人机信号识别方法. 该方法基于无人机图像传输信号 (image transmission signal, ITS) 的循环特性提取其时域参数, 采用分类决策树对无人机进行初步分类识别; 再通过分离无人机的图像传输信号与飞行控制信号 (flight control signal, FCS) 的方式分别提取其时频特征参数; 最后进行了 2 次分类识别. 实验结果表明, 对于 6 种常见无人机的通信信号, 在信噪比(signal-to-noise ratio, SNR) 为 0 dB 时平均识别准确率可达 97.4%, 说明该方法可以精确识别无人机.

关键词: 无线电侦测, 正交频分复用, 分段快速傅里叶变换 (fast Fourier transform, FFT);时频分析, 特征提取

Abstract: The rapid development of the unmanned aerial vehicle (UAV) industry has introduced security risks in low-altitude airspaces. To effectively control UAVs, a radio detection system that can identify UAV signals should be developed. To identify similar UAVs, this study proposes a UAV signal recognition method based on reclassification and separation of image transmission signals (ITSs) and flight control signals (FCSs). The pro-posed method extracts its time-domain parameters using the cyclic characteristics of the ITS and applies a classification decision tree to initially classify and identify the UAVs. Then, by separating the ITS and FCS, their time-frequency characteristic parameters are extracted, and finally, secondary classification is performed. Experimental results show

Key words: radio detection, orthogonal frequency division multiplexing (OFDM), segmented fast Fourier transform (FFT), time frequency analysis, feature extraction

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