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

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

贾宇涛^1,,
李冠华²,
潘红光^{3, 4, ,},
陈海舰²,
魏绪强^{3, 4},
白俊明^{3, 4}

1.
国能神东煤炭集团有限责任公司，陕西榆林　719315
2.
中煤科工集团常州研究院有限公司，江苏常州　213015
3.
西安科技大学电控学院，陕西西安　710054
4.
西安市电气设备状态监测与供电安全重点实验室，陕西西安　710054

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
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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

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

JIA Yutao^1
,,
LI Guanhua²,
PAN Hongguang^{3, 4
, ,},
CHEN Haijian²,
WEI Xuqiang^{3, 4},
BAI Junming^{3, 4}

1.
CHN Energy Shendong Coal Group Co., Ltd., Yulin 719315, China
2.
CCTEG Changzhou Research Institute, Changzhou 213015, China
3.
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
4.
Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi'an 710054, China

摘要

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。
- 井下人员定位 /
- UWB /
- 行人航位推算 /
- PDR /
- 融合定位 /
- 非视距环境 /
- 最小二乘法 /
- 多项式拟合 /
- CNN−LSTM /
- LOS /
- NLOS
Abstract: Most existing fusion positioning methods for ultra-wideband (UWB) and pedestrian dead reckoning (PDR) ignore the correction of positioning errors in non line of sight (NLOS) environments. The methods use simple threshold division as the basis for NLOS environment judgment, which is largely related to the positioning scene and site size. In order to solve the above problems, a fusion positioning method for underground personnel considering NLOS environment is proposed. Firstly, UWB technology is used to calculate the position of underground personnel. After obtaining the preliminary position of personnel through the trilateral positioning algorithm, the least squares method is used to optimize the position. Polynomial fitting is used to achieve the fitting between the actual value and the measured value between the base station and the tag in the NLOS environment, reducing the ranging error in the NLOS environment and improving the positioning precision. Secondly, the PDR algorithm is used for gait recognition and analysis. The PDR algorithm uses gait data collected by inertial navigation sensors to update the target position through gait recognition, step size estimation, and direction estimation. Thirdly, the convolutional neural network (CNN) - long short term memory (LSTM) network is used to analyze the features of channel impulse response (CIR) and achieve line of sight (LOS)/NLOS recognition. It solves the problem of scene limitations in NLOS environment judgment. Finally, the fusion coefficient is determined based on the LOS/NLOS recognition results to achieve the fusion of UWB and PDR positioning results. The experimental results show that after polynomial fitting, the average ranging error of UWB is reduced by 0.59 m. The average accuracy of LOS/NLOS recognition is 95.3%, and the recall rate and F₁ score are both above 90%, verifying that CNN-LSTM has good recognition performance. The average error of the fusion positioning method is 0.31 m, which is 1.57 m lower than UWB and 1.41 m lower than PDR.
- underground personnel positioning /
- UWB /
- pdestrian dead reckoning /
- PDR /
- fusion positioning /
- non line of sight environment /
- least squares method /
- polynomial fitting /
- CNN-LSTM /
- LOS /
- NLOS

HTML全文

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

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图 2 基站布局

Figure 2. Base station layout

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图 3 最小二乘法原理

Figure 3. Principle of least squares method

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图 4 定位圆存在形式

Figure 4. Existence form of positioning circle

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图 5 PDR与UWB融合定位流程

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

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图 6 测试现场

Figure 6. Test site

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图 7 训练损失

Figure 7. Training loss

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图 8 3种定位方法测试结果

Figure 8. Test results of three positioning methods

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图 9 3种定位方法误差对比

Figure 9. Comparison of errors of three positioning methods

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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

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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
测试序号	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

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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

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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

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