微多普勒辅助的城市环境无人机编队检测方法

张杰; 朱宇; 王洋

doi:10.11999/JEIT240203

微多普勒辅助的城市环境无人机编队检测方法

doi: 10.11999/JEIT240203

张杰^{1, 2},
朱宇¹,
王洋^1, ,

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
谢菲尔德大学电子电气工程系谢菲尔德 S10 2TN

基金项目: 重庆市自然科学基金(cstc2021jcyj-msxmX0634, CSTB2022NSCQ-MSX1125)，重庆市教委科学技术研究项目(KJZD-K202300607)，重庆市自然科学基金创新发展联合基金(CSTB2022NSCQ-LZX0037)

详细信息

作者简介:
张杰：男，教授，研究方向为无人机检测，小蜂窝网络规划，人工智能，天线与电波传播等

朱宇：男，硕士生，研究方向为目标检测

王洋：男，教授，研究方向为6G无线传输技术，智能反射系统，目标检测，毫米波通信，无线传输中的人工智能等

通讯作者:
王洋　wangyang@cqupt.edu.cn

中图分类号: TN957.51
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- HTML全文浏览量: 51
- PDF下载量: 45
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-25
- 修回日期: 2024-07-22
- 网络出版日期: 2024-08-03
- 刊出日期: 2024-09-26

Micro-Doppler-assisted Unmanned Aerial Vehicle Formation Detection Method in Urban Environments

ZHANG Jie^{1, 2},
ZHU Yu¹,
WANG Yang^{1
, ,}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S10 2TN, UK

Funds: The Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0634, CSTB2022NSCQ-MSX1125), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202300607), The Natural Science Foundation Innovation and Development Joint Fund Project of Chongqing (CSTB2022NSCQ-LZX0037)

摘要

摘要: 针对城市复杂环境下电磁环境复杂、多径杂波和干扰信号密集等现象，传统的无人机(UAV)检测方法通过获取回波信号提取目标多普勒信息进行检测，易受到环境影响导致检测效果不理想。该文提出微多普勒辅助的城市环境无人机编队检测方法，充分利用无人机的微动特征，能够在复杂环境下提高检测精度。首先，参数化建模表征城市复杂环境下无人机旋翼的雷达回波微多普勒信号，利用YOLOv5s检测微多普勒闪烁脉冲，有效提取位置信息；然后，引入雷达信号分选方法的脉冲重复间隔(PRI)变换，分类获得无人机编队数量；最后，利用K-means算法验证无人机编队检测方法的准确性。结果表明，所提方法在信噪比2 dB时7架无人机的检测精度高于90%，能够用于城市复杂环境存在干扰脉冲、多径效应、局部脉冲丢失的无人机编队检测。
- 无人机编队 /
- 编队检测 /
- 城市复杂环境 /
- YOLOV5s /
- 微多普勒提取
Abstract: Considering the phenomena of complex electromagnetic environment, multipath clutter and dense interference signals in complex urban environments, the traditional Unmanned Aerial Vehicle(UAV) detection method extracts the target Doppler information for detection by obtaining echo signals, which is susceptible to environmental impacts and leads to unsatisfactory detection results. A micro-Doppler-assisted formation detection method for UAVs in urban environments is proposed in this paper, which makes full use of micro-motion characteristics to improve detection accuracy. Firstly, parametric modeling characterizes the radar echo micro-Doppler signals of UAV rotor blades in urban complex environments, and detects the micro-Doppler scintillation pulses by using YOLOv5s to effectively extract the positional information. Then, the Pulse Repetition Interval (PRI) transform of the radar signal sorting method is introduced to classify and obtain the number of UAV formations. Finally, K-means algorithm is utilized to verify the accuracy of the UAV formation detection method. The results show that the proposed method has a detection accuracy of more than 90% for seven UAVs at a signal-to-noise ratio of 2 dB, and can be used for UAV formation detection in urban complex environments where there are interfering pulses, multipath effects, and local pulse loss.
- Unmanned Aerial Vehicle(UAV) formation /
- Complex urban environments /
- Formation detection /
- YOLOv5s /
- Micro-doppler extraction

HTML全文

图 1 雷达和无人机旋翼叶片的几何关系

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图 2 YOLOv5s的网络框图

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图 3 基于YOLOv5s的无人机特征提取

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图 4 分选数据的剔除结果

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图 5 无人机编队检测识别结果

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图 6 城市复杂环境下的无人机编队时频图

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图 7 无人机编队检测方法的实验结果

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表 1 无人机编队参数

仿真参数	数值
旋翼叶片数目	2
旋翼长度(${\text{cm}}$)	1.73,1.91,1.82
无人机速度(${\text{m/s}}$)	1.9,2.5,2.3
旋翼转速(${\text{r/s}}$)	3.27,3.97,3.62
初相位($^\circ $)	0,18,36
弧度($^\circ $)	45,45,45
叶片散射系数(${\text{dBsm}}$)	1,1,1

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表 2 YOLOV5s网络参数

网络参数	配置
Learning rate	0.01
Momentum	0.937
Weight decay	0.000 5
Batch size	16
Depth multiple	0.33
Width multiple	0.50

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表 3 不同无人机编队数量下的检测准确率

无人机编队数量(架)	检测准确率(%)
1	99.8
2	99.4
3	99.1
4	98.5
5	98.0
6	95.6
7	91.1
8	85.5
9	81.2

下载: 导出CSV

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