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基于UWB与PDR的井下人员融合定位方法

贾宇涛 李冠华 潘红光 陈海舰 魏绪强 白俊明

贾宇涛,李冠华,潘红光,等. 基于UWB与PDR的井下人员融合定位方法[J]. 工矿自动化,2024,50(6):96-102, 135.  doi: 10.13272/j.issn.1671-251x.2024010071
引用本文: 贾宇涛,李冠华,潘红光,等. 基于UWB与PDR的井下人员融合定位方法[J]. 工矿自动化,2024,50(6):96-102, 135.  doi: 10.13272/j.issn.1671-251x.2024010071
JIA Yutao, LI Guanhua, PAN Hongguang, et al. A fusion positioning method for underground personnel based on UWB and PDR[J]. Journal of Mine Automation,2024,50(6):96-102, 135.  doi: 10.13272/j.issn.1671-251x.2024010071
Citation: JIA Yutao, LI Guanhua, PAN Hongguang, et al. A fusion positioning method for underground personnel based on UWB and PDR[J]. Journal of Mine Automation,2024,50(6):96-102, 135.  doi: 10.13272/j.issn.1671-251x.2024010071

基于UWB与PDR的井下人员融合定位方法

doi: 10.13272/j.issn.1671-251x.2024010071
基金项目: 国家重点研发计划项目(2023YFC3009800);国家自然科学基金项目(61603295);陕西省秦创原“科学家+工程师”队伍建设项目(2022KXJ-38);西安市科技计划项目(23ZDCYJSGG0025-2022)。
详细信息
    作者简介:

    贾宇涛(1988—),男,陕西榆林人,工程师,研究方向为煤矿井下人员定位,E-mail:3159567747@qq.com

    通讯作者:

    潘红光(1983—),男,山东临沂人,副教授,研究方向为人工智能、过程控制,E-mail:hongguangpan@163.com

  • 中图分类号: TD655.3

A fusion positioning method for underground personnel based on UWB and PDR

  • 摘要: 现有超宽带(UWB)与行人航位推算(PDR)融合定位方法大多忽略了非视距(NLOS)环境下的定位误差校正,以简单的阈值划分作为NLOS环境判断依据,而阈值划分在很大程度上与定位场景及场地大小相关。针对上述问题,提出一种考虑NLOS环境的基于UWB与PDR的井下人员融合定位方法。首先,利用UWB技术进行井下人员位置解算,通过三边定位算法得到人员初步位置后,使用最小二乘法对位置进行优化,通过多项式拟合实现NLOS环境下基站和标签之间实际值和测量值之间的拟合,减小NLOS环境下的测距误差,提高定位精度。其次,采用PDR算法对步态进行识别和分析,PDR算法使用惯性导航传感器采集的步态数据,通过步态识别、步长估计和方向估计,实现目标位置的更新;然后,通过卷积神经网络(CNN)−长短期记忆(LSTM)网络分析信道脉冲响应(CIR)特征,实现视距(LOS)/NLOS识别,解决NLOS环境判断存在场景限制的问题;最后,根据LOS/NLOS识别结果确定融合系数,实现UWB和PDR定位结果融合。测试结果表明:多项式拟合后UWB平均测距误差降低0.59 m;LOS/NLOS识别的平均准确率为95.3%,召回率和F1分数均在90%以上,验证了CNN−LSTM具有较好的识别效果;融合定位方法的平均误差为0.31 m,较UWB降低1.57 m,较PDR降低1.41 m。

     

  • 图  1  基于UWB与PDR的井下人员融合定位方法原理

    Figure  1.  Principle of underground personnel fusion positioning method based on UWB and PDR

    图  2  基站布局

    Figure  2.  Base station layout

    图  3  最小二乘法原理

    Figure  3.  Principle of least squares method

    图  4  定位圆存在形式

    Figure  4.  Existence form of positioning circle

    图  5  PDR与UWB融合定位流程

    Figure  5.  The fusion positioning process of pdestrian dead reckoning(PDR) and UWB

    图  6  测试现场

    Figure  6.  Test site

    图  7  训练损失

    Figure  7.  Training loss

    图  8  3种定位方法测试结果

    Figure  8.  Test results of three positioning methods

    图  9  3种定位方法误差对比

    Figure  9.  Comparison of errors of three positioning methods

    表  1  环境识别结果

    Table  1.   Environmental recognition result %

    环境 准确率 召回率 F1分数
    环境1 95.0 92.1 91.9
    环境2 95.2 91.3 93.0
    环境3 95.8 91.6 91.7
    环境4 95.1 92.6 91.5
    平均值 95.3 91.9 92.0
    下载: 导出CSV

    表  2  多项式拟合前后误差对比

    Table  2.   Comparison of errors before and after polynomial fitting

    测试序号 UWB测距误差/m
    拟合前 拟合后
    1 1.29 0.86
    2 1.23 0.88
    3 1.32 0.91
    4 1.49 0.97
    5 1.38 0.65
    6 1.44 0.71
    7 1.19 0.59
    8 1.63 0.84
    9 1.51 0.78
    10 1.20 0.63
    平均值 1.37 0.78
    下载: 导出CSV

    表  3  3种定位方法误差统计结果

    Table  3.   Statistical results of errors of three positioning methods m

    定位方法 最大误差 最小误差 平均误差
    UWB 3.71 0.21 1.88
    PDR 3.75 0.05 1.72
    UWB+PDR 1.19 0.06 0.31
    下载: 导出CSV

    表  4  融合方法误差对比

    Table  4.   Comparison of errors of fusion methods

    融合方法 平均定位误差/m
    抗差卡尔曼滤波 1.420
    扩展卡尔曼滤波 0.475
    自适应扩展卡尔曼滤波 0.330
    视距分析融合 0.310
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-22
  • 修回日期:  2024-06-18
  • 网络出版日期:  2024-07-10

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