多旋翼无人机载4D成像雷达生命体征感知方法

李志; 唐成垚; 戴永鹏; 金添

doi:10.12000/JR24128

多旋翼无人机载4D成像雷达生命体征感知方法

DOI: 10.12000/JR24128

李志¹,
唐成垚²,
戴永鹏²,
金添^2, ,

1.
陆军工程大学通信士官学校南京 210007
2.
国防科技大学电子科学学院长沙 410073

基金项目: 重庆市自然科学基金(CSTB2024NSCQ-MSX1143)

详细信息

作者简介:
李　志，博士，讲师，主要研究方向为雷达成像、雷达信号处理与机器学习

唐成垚，博士生，主要研究方向为穿墙雷达信号处理与机器学习

戴永鹏，博士，讲师，主要研究方向为MIMO阵列雷达成像与图像增强

金　添，教授，主要研究方向为新体制雷达系统、智能感知与处理

通讯作者:
金添 tianjin@nudt.edu.cn

责任主编：郭世盛 Corresponding Editor: GUO Shisheng
中图分类号: TN957
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- 文章访问数: 48
- HTML全文浏览量: 25
- PDF下载量: 162
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-21
- 修回日期: 2024-09-25

Multirotor UAV-borne Vital Signs Sensing Using 4D Imaging Radar

1.
Communication Noncommissioned Officer School, Army Engineering University of PLA, Nanjing 210007, China
2.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: Natural Science Foundation of Chongqing China (CSTB2024NSCQ-MSX1143)

More Information

Corresponding author: JIN Tian, tianjin@nudt.edu.cn

摘要

摘要: 无人机载雷达具有高机动灵活的特点，可解决传统非接触式生命体征感知中存在的探测范围小和探测场景受限等问题。该项研究工作将4D成像雷达搭载于多旋翼无人机上，提出一种基于点云配准的无人机载4D雷达生命体征感知方法。该方法通过对雷达点云进行配准和运动补偿，消除无人机在悬停状态时的运动误差干扰，进而对齐人体目标后实现生命体征信号的获取。仿真实验结果表明该方法能够对齐4D成像雷达点云序列，有效抑制无人机的运动干扰，从而准确提取人体目标的呼吸和心跳信号，为无人机载非接触式生命体征感知提供了一种新的技术途径。
- 生命体征 /
- 4D成像雷达 /
- 无人机 /
- 点云 /
- 配准
Abstract: Unmanned Aerial Vehicle (UAV)-borne radar technology can solve the problems associated with noncontact vital sign sensing, such as limited detection range, slow moving speed, and difficult access to certain areas. In this study, we mount a 4D imaging radar on a multirotor UAV and propose a UAV-borne radar-based method for sensing vital signs through point cloud registration. Through registration and motion compensation of the radar point cloud, the motion error interference of UAV hovering is eliminated; vital sign signals are then obtained after aligning the human target. Simulation results show that the proposed method can effectively align the 4D radar point cloud sequence and accurately extract the respiration and heartbeat signals of human targets, thereby providing a way to realize UAV-borne vital sign sensing.
- Vital signs /
- 4D imaging radar /
- Unmanned Aerial Vehicle (UAV) /
- Point cloud /
- Registration

HTML全文

图 1 无人机载4D雷达生命体征感知场景示意

Figure 1. Vital signs sensing principle of the UAV-borne 4D radar

下载: 全尺寸图片幻灯片

图 2 无人机载4D成像雷达生命体征感知信号处理流程

Figure 2. Signal processing flow of vital signs sensing with the UAV-borne 4D radar

下载: 全尺寸图片幻灯片

图 3 雷达平台运动分析

Figure 3. Motion analysis of the UAV-borne 4D radar platform

下载: 全尺寸图片幻灯片

图 4 运动补偿方法信号处理流程

Figure 4. Signal processing flow of the proposed motion compensation method

下载: 全尺寸图片幻灯片

图 5 雷达系统结构与MIMO天线等效虚拟阵列

Figure 5. Structure of the UAV-borne 4D radar system and its equivalent virtual array

下载: 全尺寸图片幻灯片

图 6 仿真实验场景

Figure 6. Simulated experimental scene

下载: 全尺寸图片幻灯片

图 7 点云配准前和配准后结果

Figure 7. Results before and after point cloud registration

下载: 全尺寸图片幻灯片

图 8 雷达运动轨迹估计

Figure 8. Motion trajectory estimation of the UAV-borne 4D radar

下载: 全尺寸图片幻灯片

图 9 运动补偿前提取的人体目标生命体征信号结果

Figure 9. Extracted vital sign signals of human target before motion compensation

下载: 全尺寸图片幻灯片

图 10 运动补偿后提取的人体目标生命体征信号结果

Figure 10. Extracted vital sign signals of human target after motion compensation

下载: 全尺寸图片幻灯片

表 1 无人机载4D成像雷达主要参数

Table 1. Key parameters of the UAV-borne 4D radar prototype

参数	参数值
中心频率	67 GHz
信号带宽	1 GHz
发射功率	–10 dBm
帧率	30 Hz
阵元数量	20 Tx, 20 Rx
阵列尺寸	6 cm × 6 cm

下载: 导出CSV

表 2 六自由度估计误差统计

Table 2. Estimation error statistics of the 6DOF

指标	MEAN	RMSE
垂直偏移(m)	0.0031	0.0140
水平偏移X (m)	0.0069	0.0261
水平偏移Y (m)	0.0071	0.0249
俯仰角(°)	0.0372	0.1108
偏航角(°)	0.0515	0.1932
翻滚角(°)	0.0355	0.1099

下载: 导出CSV

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