超宽带雷达物联网呼吸心跳体征提取

超宽带雷达物联网呼吸心跳体征提取

摘要

超宽带雷达具有发射功率低，穿透性强，灵敏度高，目标识别能力好等优势。该雷达在生命体征获取，目标数量识别，目标定位，目标追踪等方面有着广泛应用。在可见度低的环境，如云雾，雨雪，烟雾等天气情况下仍能保持良好性能，同时可以避免图像识别引发的个人隐私侵犯的问题。基于超宽带雷达的呼吸心跳生命体征提取是超宽带雷达的重要应用场景之一。物联网（Internet of Things，IoT）是以互联网、传统电信网等作为载体，将各个能行使独立功能的终端设备联系起来，通过远程控制等方法实现互联互通的网络。构建超宽带雷达物联网可以有效提高生命体征提取的精确度，同时获取目标不同运动状态的数据。

本文结合国内外研究进展，深入研究了超宽带雷达的特性，在实测之前对雷达脉冲的发射和接收过程进行仿真；仿真后搭建了超宽带雷达物联网，并在车内环境下对对驾驶员的生命体征进行检测和提取。针对车内复杂的多径反射现象，对实测数据使用经验模态分解和变分模态分解两种算法进行处理，提取生命体征，并进行了比较。对于影响提取精度的驾驶员相关行为，先采用深度学习的方法识别运动模式，在运动情况下采用运动干扰消除算法，保证提取的精确度。结果表明，静止状态识别的准确度可以达到93.3%，运动状态准确度为88.5%。

本文的工作主要有：对脉冲超宽带雷达生命体征检测过程进行仿真；搭建超宽带雷达物联网进行数据采集并提取生命体征；运用深度学习方法识别运动模式，对运动干扰消除方法展开讨论；以上工作内容为超宽带雷达物联网的进一步推广和应用打下了基础。

关键词超宽带雷达物联网呼吸心跳体征信号处理仿真卷积神经网络深度学习

Vital Sign detection with Internet of UWB Radars

ABSTRACT

Ultra-wideband radar has the advantages of low transmission power, strong penetrability, high sensitivity and good target recognition ability. The radar has a wide range of applications in terms of vital signs acquisition, target quantity identification, target location, and target tracking. In a low visibility environment, such as clouds, rain, snow, smoke and other weather conditions can still maintain good performance, while avoiding the problem of personal privacy violations caused by image recognition. Respiratoryandheartbeat vital sign extraction based on UWB radar is one of the important application scenarios of UWB radar. The Internet of Things (IoT) is based on the Internet, traditional telecommunication networks, etc., and connects various terminal devices that can perform independent functions, and realizes interconnection and interconnection through remote control and other methods. The construction of UWB radar Internet of Things can effectively improve the accuracy of vital sign extraction, and at the same time acquire data of different motion states of the target.

Based on the research progress at home and abroad, this paper deeply studies the characteristics of UWB radar, and simulates the transmission and reception process of radar pulse before the actual measurement. After the simulation, the IoUWB is built. The vital signs of the driver are detected and extracted in the vehicle environment. For the complex multipath reflection phenomenon in the car, the data measured in car is processed by empirical mode decomposition and variational mode decomposition, and the two methods are extracted and compared based on the vital signs. For the driver-related behaviors that affect the extraction accuracy, the deep learning method is used to identify the motion mode, and the motion interference elimination algorithm is used in the motion situation to ensure the accuracy of the extraction. The results show that the accuracy of the stationary state recognition can reach 93.3%, and the accuracy of the motion state is 88.5%.

The main work of this paper is: simulation of the vital sign detection process of pulse UWB radar; construction of IoUWB for data acquisition and extraction of vital signs; use of deep learning method to identify the motion mode, discuss the method of motion interference elimination; The content lays the foundation for the further promotion and application of the IoUWB.

KEY WORDS ultra-wide band radar Internet of Thingsrespiratory heartbeat signal processing convolutional neural network deep learning