Volume 50 Issue 1
Jan.  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(1): 25-34
LI Mingfeng, LI Yan, LIU Yong, et al. Underground personnel positioning system based on 5G+UWB and inertial navigation technology[J]. Journal of Mine Automation，2024，50（1）：25-34.  doi: 10.13272/j.issn.1671-251x.2023100066
Citation: LI Mingfeng, LI Yan, LIU Yong, et al. Underground personnel positioning system based on 5G+UWB and inertial navigation technology[J]. Journal of Mine Automation，2024，50（1）：25-34.  doi: 10.13272/j.issn.1671-251x.2023100066
shu

Underground personnel positioning system based on 5G+UWB and inertial navigation technology

doi: 10.13272/j.issn.1671-251x.2023100066
  • 1.

    Yanbei Coal Mine, Gansu Huating Coal Power Co., Ltd., Huating 744100, China

  • 2.

    College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2023-10-23
  • Rev Recd Date: 2024-01-16
    • Available Online: 2024-01-31
    Abstract
  • In practical applications of coal mine personnel positioning systems, there are problems of insufficient equipment computing power and storage resources. The problems result in preventing the use of complex ranging and positioning algorithms, inadequate real-time transmission and response performance of positioning data, and significant human and material resource losses in system deployment. In order to solve the above problems, a new underground personnel positioning system based on 5G+UWB and inertial navigation technology is proposed. The system deploys UWB positioning base stations with low energy consumption and strong anti-interference capability at the end. The positioning base station is connected to the 5G base station in a cascaded manner. The positioning base station collects UWB and inertial navigation data, and uses the 5G network to transmit it back to the computing platform. The positioning information is solved and stored on the computing platform. The inertial navigation based personnel position estimation is used as the predicted value. The UWB based trilateral positioning algorithm is used to obtain personnel position estimation as the observed value. The Kalman filter is used to fuse the predicted and observed values to reduce positioning errors. The testing system is built at the main experimental base of the coal mine, simulating the real underground environment of the coal mine, and conducting comparative experiments. The results show the following points. ①In the x-axis direction and the y-axis direction, the coincidence degree between the position information obtained by the Kalman filter algorithm of the fusion inertial navigation and the real position information is the highest, indicating that the position information obtained by the Kalman filter algorithm of the fusion inertial navigation is closest to the real position, and the average error is 22.192 cm. ② The position information of the underground personnel positioning system combined with 5G + UWB and inertial navigation technology has the highest coincidence degree with the real position information, and the error is [15 cm, 20 cm], with a maximum average error of 26 cm on the x-axis and 24 cm on the y-axis, exceeding the precision of most current underground personnel positioning systems.

     

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    • References(25)
  • [1]
    刘宇,李瑶,路永乐,等. 基于MEMS传感器组合的行人室内高度定位算法[J]. 压电与声光,2019,41(5):690-693,746.

    LIU Yu,LI Yao,LU Yongle,et al. Pedestrian indoor height localization algorithm based on MEMS sensor combination[J]. Piezoelectrics & Acoustooptics,2019,41(5):690-693,746.
    [2]
    汪义庭. 基于UWB的无线室内定位系统设计与实现[D]. 淮南:安徽理工大学,2019.

    WANG Yiting. Design and implementation of wireless indoor positioning system based on UWB[D]. Huainan:Anhui University of Science and Technology,2019.
    [3]
    WANG Yixin,YE Qiang,CHENG Jie,et al. RSSI-based bluetooth indoor localization[C]. 11th International Conference on Mobile Ad-hoc and Sensor Networks ,Shenzhen,2015:165-171.
    [4]
    LI Qiyue,LI Wei,SUN Wei,et al. Fingerprint and assistant nodes based WiFi localization in complex indoor environment[J]. IEEE Access,2016,4:2993-3004. doi: 10.1109/ACCESS.2016.2579879
    [5]
    GHOLOOBI A,STAVROU S. Accelerating TOA/TDOA packet based localization methods[C]. IEEE Conference on Wireless Sensors ,Subang,2014:31-35.
    [6]
    吕瑞杰. 煤矿井下UWB信号路径损耗测量及中心频率选择[J]. 工矿自动化,2023,49(4):147-152.

    LYU Ruijie. Measurement of UWB signal path loss and center frequency selection in underground coal mines[J]. Journal of Mine Automation,2023,49(4):147-152.
    [7]
    ZENG Zhuoqi,LIU S,WANG Lei. UWB NLOS identification with feature combination selection based on genetic algorithm[C]. IEEE International Conference on Consumer Electronics,Las Vegas,2019. DOI: 10.1109/ICCE.2019.8662065.
    [8]
    孙建强,尚俊娜,刘新华,等. 一种基于模糊推理的改进加权KNN定位算法[J]. 传感技术学报,2020,33(6):882-888. doi: 10.3969/j.issn.1004-1699.2020.06.015

    SUN Jianqiang,SHANG Junna,LIU Xinhua,et al. An improved weighted KNN location algorithm based on fuzzy reasoning[J]. Chinese Journal of Sensors and Actuators,2020,33(6):882-888. doi: 10.3969/j.issn.1004-1699.2020.06.015
    [9]
    孙晔,肖竹,李小蓓,等. UWB定位中基于神经网络的TDOA解算方法[J]. 航空计算技术,2019,49(2):6-10. doi: 10.3969/j.issn.1671-654X.2019.02.002

    SUN Ye,XIAO Zhu,LI Xiaobei,et al. Neural network based TDOA calculation algorithm in a UWB system[J]. Aeronautical Computing Technique,2019,49(2):6-10. doi: 10.3969/j.issn.1671-654X.2019.02.002
    [10]
    STRAETEN M,AHAMED M J. Intuitive ultrasonic INS augmentation for pedestrian path tracking and navigation[J]. Sensors and Actuators A Physical,2019. DOI: 10.1016/j.sna.2019.111641.
    [11]
    LIU Fei,LI Xin,WANG Jian,et al. An adaptive UWB/MEMS-IMU complementary kalman filter for indoor location in NLOS environment[J]. Remote Sensing,2019,11(22). DOI: 10.3390/rs11222628.
    [12]
    FENG Daquan,WANG Chunqi,HE Chunlong,et al. Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation[J]. IEEE Internet of Things Journal,2020,7(4):3133-3146. doi: 10.1109/JIOT.2020.2965115
    [13]
    WANG Tianyu,HU Keke,LI Zhihang,et al. A semi-supervised learning approach for UWB ranging error mitigation[J]. IEEE Wireless Communications Letters,2021,10(3):688-691. doi: 10.1109/LWC.2020.3046531
    [14]
    SANG C L,STEINHAGEN B,HOMBURG J D,et al. Identification of NLOS and multi-path conditions in UWB localization using machine learning methods[J]. Applied Sciences,2020,10(11). DOI: 10.3390/app10113980.
    [15]
    FERREIRA A,FERNANDES D,BRANCO S,et al. Feature selection for real-time NLOS identification and mitigation for body-mounted UWB transceivers[J]. IEEE Transactions on Instrumentation and Measurement,2021,70. DOI: 10.1109/TIM.2021.3070619.
    [16]
    TRAN D H,CHUNG B D,JANG Y M. GAN-based data augmentation for UWB NLOS identification using machine learning[C]. International Conference on Artificial Intelligence in Information and Communication ,Jeju Island,2022:417-420.
    [17]
    JIANG Changhui,SHEN Jichun,CHEN Shuai,et al. UWB NLOS/LOS classification using deep learning method[J]. IEEE Communications Letters,2020,24(10):2226-2230. doi: 10.1109/LCOMM.2020.2999904
    [18]
    YANG Bo,LI Jun,SHAO Zhanpeng,et al. Robust UWB indoor localization for NLOS scenes via learning spatial-temporal features[J]. IEEE Sensors Journal,2022,22(8):7990-8000. doi: 10.1109/JSEN.2022.3156971
    [19]
    CHEN Shanzhi,KANG Shaoli. A tutorial on 5G and the progress in China[J]. Frontiers of Information Technology & Electronic Engineering,2018,19(3):309-321.
    [20]
    CHU Yeping,LIN Pan,LENG Kaijun,et al. Research on key technologies of service quality optimization for industrial IoT 5G network for intelligent manufacturing[J]. The International Journal of Advanced Manufacturing Technology,2022,123(7/8). DOI: 10.1007/S00170-022-10418-6.
    [21]
    霍振龙. 5G专网技术及煤矿5G专网方案分析[J]. 工矿自动化,2022,48(11):6-10,19.

    HUO Zhenlong. Analysis of 5G private network technology and coal mine 5G private network scheme[J]. Journal of Mine Automation,2022,48(11):6-10,19.
    [22]
    ELSANHOURY M,MAKELA P,KOLJONEN J,et al. Precision positioning for smart logistics using ultra-wideband technology-based indoor navigation:a review[J]. IEEE Access,2022,10:44413-44445. doi: 10.1109/ACCESS.2022.3169267
    [23]
    LI Yinya,QI Guoqing,SHENG Andong. Performance metric on the best achievable accuracy for hybrid TOA/AOA target localization[J]. IEEE Communications Letters,2018,22(7):1474-1477. doi: 10.1109/LCOMM.2018.2833544
    [24]
    蓝威涛,张卫强,罗健宇. 一种自适应智能三边定位算法的设计与实现[J]. 传感技术学报,2017,30(7):1089-1094.

    LAN Weitao,ZHANG Weiqiang,LUO Jianyu. Design and implementation of adaptive intelligent trilateral localization algorithm[J]. Chinese Journal of Sensors and Actuators,2017,30(7):1089-1094.
    [25]
    王鼎杰,吕汉峰,吴杰. 基于微惯导随机误差时间序列建模的改进组合导航方法[J]. 国防科技大学学报,2016,38(6):64-69. doi: 10.11887/j.cn.201606011

    WANG Dingjie,LYU Hanfeng,WU Jie. ARMA-based stochastic modeling method for improving the performance of low-cost MIMU/GNSS integration[J]. Journal of National University of Defense Technology,2016,38(6):64-69. doi: 10.11887/j.cn.201606011
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