Measuring Micrometer-Level Vibrations With mmWave Radar

被引:24
|
作者
Guo, Junchen [1 ]
He, Yuan [1 ]
Jiang, Chengkun [1 ]
Jin, Meng [1 ]
Li, Shuai [1 ]
Zhang, Jia [1 ]
Xi, Rui [1 ]
Liu, Yunhao [1 ]
机构
[1] Tsinghua Univ, Sch Software & Automation Dept, Beijing, Peoples R China
基金
国家重点研发计划;
关键词
Vibrations; Vibration measurement; Radar; Measurement errors; Frequency measurement; Signal to noise ratio; Sensors; Wireless sensing; millimeter wave; vibration measurement;
D O I
10.1109/TMC.2021.3118349
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vibrations measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.
引用
收藏
页码:2248 / 2261
页数:14
相关论文
共 50 条
  • [31] Micrometer Level Positioning Design
    Chen, Yung-Yue
    Lan, Yu-Jen
    Wang, Ming-Cheng
    Zhang, Yi-Qing
    Tsai, Chun-Jung
    2020 INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS), 2020,
  • [32] A Real-Time, Frame-Level Platform Vibration Compensation Approach for mmWave Radar Systems
    Poole, Nikhil
    Hor, Soheil
    Arbabian, Amin
    2021 18TH EUROPEAN RADAR CONFERENCE (EURAD), 2021, : 181 - 184
  • [33] Interpersonal Distance Tracking with mmWave Radar and IMUs
    Dai, Yimin
    Shuai, Xian
    Tan, Rui
    Xing, Guoliang
    PROCEEDINGS OF THE 2023 THE 22ND INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS, IPSN 2023, 2023, : 123 - 135
  • [34] Contactless Blood Pressure Monitoring with mmWave Radar
    Ran, You
    Zhang, Dongheng
    Chen, Jinbo
    Hu, Yang
    Chen, Yan
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 541 - 546
  • [35] MiliPoint: A Point Cloud Dataset for mmWave Radar
    Cui, Han
    Zhong, Shu
    Wu, Jiacheng
    Shen, Zichao
    Dahnoun, Naim
    Zhao, Yiren
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [36] Contactless Walking Recognition based on mmWave RADAR
    Senigagliesi, Linda
    Ciattaglia, Gianluca
    Gambi, Ennio
    2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2020, : 914 - 917
  • [37] Face Recognition using mmWave RADAR imaging
    Challa, Muralidhar Reddy
    Kumar, Abhinav
    Cenkeramaddi, Linga Reddy
    2021 IEEE INTERNATIONAL SYMPOSIUM ON SMART ELECTRONIC SYSTEMS (ISES 2021), 2021, : 319 - 322
  • [38] Capturing Human Pose Using mmWave Radar
    Li, Guangzheng
    Zhang, Ze
    Yang, Hanmei
    Pan, Jin
    Chen, Dayin
    Zhang, Jin
    2020 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), 2020,
  • [39] mmWave Antenna Array Unit for Imaging Radar
    Nikishov, Artem
    Evtyushkin, Gennady
    Lukyanov, Anton
    Lee, Jaesup
    Kim, Jongseok
    Sugiura, Tsuyoshi
    2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
  • [40] Contactless GSR Sensing Using mmWave Radar
    Xu, Xiaohan
    Zhang, Dongheng
    Chen, Jinbo
    Wu, Zhi
    Sun, Qibin
    Chen, Yan
    IEEE SENSORS JOURNAL, 2022, 22 (24) : 24264 - 24275