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基于多维信号特征的无人机探测识别方法

聂伟 戴琪霏 杨小龙 王平 周牧 周超

聂伟, 戴琪霏, 杨小龙, 王平, 周牧, 周超. 基于多维信号特征的无人机探测识别方法[J]. 电子与信息学报, 2024, 46(3): 1089-1099. doi: 10.11999/JEIT230302
引用本文: 聂伟, 戴琪霏, 杨小龙, 王平, 周牧, 周超. 基于多维信号特征的无人机探测识别方法[J]. 电子与信息学报, 2024, 46(3): 1089-1099. doi: 10.11999/JEIT230302
NIE Wei, DAI Qifei, YANG Xiaolong, WANG Ping, ZHOU Mu, ZHOU Chao. Unmanned Aerial Vehicle Detection and Recognition Method Based on Multi-dimensional Signal Feature[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1089-1099. doi: 10.11999/JEIT230302
Citation: NIE Wei, DAI Qifei, YANG Xiaolong, WANG Ping, ZHOU Mu, ZHOU Chao. Unmanned Aerial Vehicle Detection and Recognition Method Based on Multi-dimensional Signal Feature[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1089-1099. doi: 10.11999/JEIT230302

基于多维信号特征的无人机探测识别方法

doi: 10.11999/JEIT230302
基金项目: 国家自然科学基金(62101085),重庆市教委科技研究项目(KJQN202000630),重庆市九龙坡区科技计划(2022-02-005-Z),民航飞行技术与飞行安全重点实验室开放项目(FZ2021KF08)
详细信息
    作者简介:

    聂伟:男,讲师,研究方向为射频识别技术、室内定位技术、电磁场与微波技术等

    戴琪霏:男,硕士生,研究方向为室内定位技术、射频指纹

    杨小龙:男,讲师,研究方向为室内入侵检测技术、高维信号处理、机器学习等

    王平:男,副教授,研究方向为通信天线新技术、信息感知与无线传输、毫米波无线通信

    周牧:男,教授,博士生导师,研究方向为无线定位与导航技术、信号处理与检测技术、机器学习与信息融合技术等

    周超:男,教授,研究方向为民用无人机、民航电磁环境效应

    通讯作者:

    杨小龙 yangxiaolong@cqupt.edu.cn

  • 中图分类号: TN957.51; V279

Unmanned Aerial Vehicle Detection and Recognition Method Based on Multi-dimensional Signal Feature

Funds: The National Natural Science Foundation of China (62101085), the Chongqing Education Commission Science and Technology Research Project (KJQN202000630), the Science and Technology Research Project of Chongqing Jiulongpo District (2022-02-005-Z), the Open Project of the Key Laboratory of Civil Aviation Flight Technology and Flight Safety (FZ2021KF08)
  • 摘要: 如今,无人机(UAVs)在军用民用领域得到大规模应用,在无人机带来便利的同时也带来了巨大的安全隐患。针对无人机的探测识别技术逐渐成为研究热点,传统的无人机探测方法主要是通过获取雷达回波信号、无人机声音信号和光电信号的方式对无人机进行探测。然而,这类方法往往容易受到环境影响具有一定的局限性,无法对无人机进行精确的定位和识别。该文提出一种基于多维信号特征的无人机识别方法,该方法首先通过自适应三角阈值法从接收到的无线信号中探测并筛选出无人机信号,同时解析获取的无线信号的信道状态信息(CSI)。然后,利用正交匹配追踪算法(OMP)进行参数估计来获取无人机的位置信息对无人机进行定位。最后,提取无人机信号中的盒维数和径向积分双谱(RIB)来对无人机进行分类识别。通过实验,该方法对无人机的3维定位精度小于1 m,对无人机的分类识别精度最高能达到100%。
  • 图  1  1倍阈值示意图

    图  2  信号预处理前后对比图

    图  3  无人机定位模型

    图  4  飞行高度20 m定位误差示意图

    图  5  飞行高度30 m定位误差示意图

    图  6  飞行高度50 m定位误差示意图

    图  7  tello和DJI无人机拟合曲线示意图

    图  8  无人机盒维数对比图

    图  9  双谱估计3维图

    图  10  RIB积分路径图

    图  11  贡献率选择对比图

    图  12  分类结果示意图

    图  13  PCA降维前后对比图

    表  1  不同权值检测结果对比图(%)

    3倍阈值5倍阈值15倍阈值
    检测率100.0100.099.5
    虚警率(FAR)0.1300.0320
    下载: 导出CSV

    表  2  飞行高度20 m算法对比图

    算法置信度(%)2维定位误差(m)3维定位误差(m)
    RAP-MUSIC500.302.05
    OMP500.300.65
    下载: 导出CSV

    表  3  飞行高度50 m算法对比图

    算法置信度(%)2维定位误差(m)3维定位误差(m)
    RAP-MUSIC500.301.95
    OMP500.250.66
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-19
  • 修回日期:  2023-08-10
  • 网络出版日期:  2023-08-17
  • 刊出日期:  2024-03-27

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