Volume 50 Issue 6
Jun.  2024
Turn off MathJax
Article Contents
WANG Taiji. A method for estimating the step size of underground personnel based on generative adversarial networks[J]. Journal of Mine Automation,2024,50(6):103-111.  doi: 10.13272/j.issn.1671-251x.2024020039
Citation: WANG Taiji. A method for estimating the step size of underground personnel based on generative adversarial networks[J]. Journal of Mine Automation,2024,50(6):103-111.  doi: 10.13272/j.issn.1671-251x.2024020039

A method for estimating the step size of underground personnel based on generative adversarial networks

doi: 10.13272/j.issn.1671-251x.2024020039
  • Received Date: 2024-02-26
  • Rev Recd Date: 2024-06-25
  • Available Online: 2024-07-10
  • In response to the problems of cumulative errors in step size estimation and the large sample size required by traditional deep learning methods in the pedestrian dead reckoning (PDR) based underground personnel positioning system in coal mines, a step size estimation method for underground personnel based on generative adversarial network (GAN) is proposed. The GAN model mainly includes two parts: generative model and discriminative model, both of which are implemented using deep neural networks (DNNs). The generative model aims to generate continuous result distributions (i.e. labels) based on input data. Its output layer uses a linear activation function to preserve the linear features of the network, allowing the model to predict the step size of any personnel during walking. The discriminant model aims to distinguish whether the input data and labels are real labels or labels generated by the generator. Its output layer uses a Sigmoid activation function to achieve binary classification of results. After determining the generative model and discriminant model, the GAN model combines two models for training. By constructing and optimizing the dynamic competition between the generator and discriminator, the generator can learn to generate more realistic and indistinguishable data samples in continuous iterations. The experimental results show that under the same training and testing sets, the average error of the GAN model is 0.14 m, and the standard deviation and root mean square error are both smaller than those of the DNNs model, with the minimum values being 0.74 m. The outdoor test results show that the GAN based underground personnel step estimation method has a minimum error of 3.21% and a maximum error of 4.79% in uphill and downhill scenarios. Compared to uphill and downhill scenarios, the error in playground scenarios is smaller, with a maximum error of 1.91%.

     

  • loading
  • [1]
    包建军,霍振龙,徐炜,等. 一种高精度井下人员无线定位方法[J]. 工矿自动化,2009,35(10):18-21.

    BAO Jianjun,HUO Zhenlong,XU Wei,et al. A wireless location method with high precision for underground personnel tracking[J]. Industry and Mine Automation,2009,35(10):18-21.
    [2]
    LEVI R W,JUDD T. Dead reckoning navigational system using accelerometer to measure foot impacts:US5583776[P]. 1996-12-10.
    [3]
    GUO Shuli,ZHANG Yitong,GUI Xinzhe,et al. An improved PDR/UWB integrated system for indoor navigation applications[J]. IEEE Sensors Journal,2020,20(14):8046-8061. doi: 10.1109/JSEN.2020.2981635
    [4]
    WANG Hucheng,ZHANG Lei,WANG Zhi,et al. Pals:high-accuracy pedestrian localization with fusion of smartphone acoustics and PDR[EB/OL]. [2024-01-12]. https://ceur-ws.org/Vol-2498/short38.pdf.
    [5]
    DÍEZ L E,BAHILLO A,OTEGUI J,et al. Step length estimation methods based on inertial sensors:a review[J]. IEEE Sensors Journal,2018,18(17):6908-6926. doi: 10.1109/JSEN.2018.2857502
    [6]
    VEZOČNIK M,JURIC M B. Average step length estimation models' evaluation using inertial sensors:a review[J]. IEEE Sensors Journal,2019,19(2):396-403. doi: 10.1109/JSEN.2018.2878646
    [7]
    KONE Y,ZHU Ni,RENAUDIN V. Zero velocity detection without motion pre-classification:uniform AI model for all pedestrian motions (UMAM)[J]. IEEE Sensors Journal,2022,22(6):5113-5121. doi: 10.1109/JSEN.2021.3099860
    [8]
    MIYAZAKI S. Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope[J]. IEEE Transactions on Bio-Medical Engineering,1997,44(8):753-759. doi: 10.1109/10.605434
    [9]
    TJHAI C,O'KEEFE K. Step-size estimation using fusion of multiple wearable inertial sensors[C]. International Conference on Indoor Positioning and Indoor Navigation,Sapporo,2017:1-8.
    [10]
    XIA Hao,ZUO Jinbo,LIU Shuo,et al. Indoor localization on smartphones using built-in sensors and map constraints[J]. IEEE Transactions on Instrumentation and Measurement,2019,68(4):1189-1198. doi: 10.1109/TIM.2018.2863478
    [11]
    WANG Hucheng,XUE Can,WANG Zhi,et al. Smartphone-based pedestrian NLOS positioning based on acoustics and IMU parameter estimation[J]. IEEE Sensors Journal,2022,22(23):23095-23108. doi: 10.1109/JSEN.2022.3185248
    [12]
    HANNINK J,KAUTZ T,PASLUOSTA C F,et al. Mobile stride length estimation with deep convolutional neural networks[J]. IEEE Journal of Biomedical and Health Informatics,2018,22(2):354-362. doi: 10.1109/JBHI.2017.2679486
    [13]
    SUI J D,CHANG T S. IMU based deep stride length estimation with self-supervised learning[J]. IEEE Sensors Journal,2021,21(6):7380-7387. doi: 10.1109/JSEN.2021.3049523
    [14]
    JIN H,KANG I,CHOI G,et al. Wearable sensor-based step length estimation during overground locomotion using a deep convolutional neural network[C]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Mexico,2021:4897-4900.
    [15]
    DÍAZ S,DISDIER S,LABRADOR M A. Step length and step width estimation using wearable sensors[C]. The 9th IEEE Annual Ubiquitous Computing,Electronics & Mobile Communication Conference,New York,2018:997-1001.
    [16]
    HAN K,YU S M,KO S W,et al. Waveform-guide transformation of IMU measurements for smartphone-based localization[J]. IEEE Sensors Journal,2023,23(17):20379-20389. doi: 10.1109/JSEN.2023.3298713
    [17]
    孙延鑫,毛善君,苏颖,等. 改进的井下人员定位PDR算法研究[J]. 工矿自动化,2021,47(1):43-48.

    SUN Yanxin,MAO Shanjun,SU Ying,et al. Research on improved PDR algorithm for underground personnel positioning[J]. Industry and Mine Automation,2021,47(1):43-48.
    [18]
    WANG Qu,LUO Haiyong,YE Langlang,et al. Personalized stride-length estimation based on active online learning[J]. IEEE Internet of Things Journal,2020,7(6):4885-4897. doi: 10.1109/JIOT.2020.2971318
    [19]
    郭倩倩,崔丽珍,杨勇,等. 基于LSTM个性化步长估计的井下人员精准定位PDR算法[J]. 工矿自动化,2022,48(1):33-39.

    GUO Qianqian,CUI Lizhen,YANG Yong,et al. PDR algorithm for precise positioning of underground personnel based on LSTM personalized step size estimation[J]. Industry and Mine Automation,2022,48(1):33-39.
    [20]
    VOIGHT J. Quaternion algebras[M]. Berlin:Springer,2021.
    [21]
    DIEBEL J. Representing attitude:euler angles,unit quaternions,and rotation vectors[J]. Matrix,2006,58(15):1-35.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(15)  / Tables(6)

    Article Metrics

    Article views (105) PDF downloads(10) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return