Underground personnel positioning system based on 5G+UWB and inertial navigation technology
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摘要: 针对煤矿井下人员定位系统在实际应用中存在因设备算力与存储资源不足导致无法使用复杂测距与定位算法，定位数据即时传输与响应性能不足，在系统部署方面人力物力损耗较大等问题，提出了一种基于5G+UWB和惯导技术的井下人员定位系统。在末端部署能耗低、抗干扰性强的UWB定位基站，定位基站与5G基站以级联的方式连接，定位基站采集UWB与惯导数据，利用5G网络回传至计算平台，在计算平台上完成定位信息的解算和存储。将基于惯导的人员位置估计作为预测值，将基于UWB的三边定位算法获取的人员位置估计作为观测值，利用卡尔曼滤波器将预测值和观测值进行融合，降低定位误差。在煤矿主体实验基地搭建测试系统，模拟真实煤矿井下环境并进行对比实验。结果表明：① 在x轴和y轴，融合惯导的卡尔曼滤波算法得出的位置信息和真实位置信息的重合度最高，说明融合惯导的卡尔曼滤波算法得出的位置信息最接近真实位置,平均误差为22.192 cm。② 5G+UWB和惯导技术组合的井下人员定位系统的位置信息和真实位置信息的重合度最高，误差为[15 cm，20 cm]，x轴最大平均误差为26 cm，y轴最大平均误差为24 cm，超过目前大多数井下人员定位系统精度。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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Key words:
- underground personnel positioning /
- 5G /
- UWB /
- inertial navigation technology /
- Kalman filtering
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图 4 基于5G+UWB和惯导的井下人员定位系统架构
Figure 4. Network structure of underground personnel positioning system based on 5G + UWB and inertial navigation
表 1 煤矿井下人员定位系统架构
Table 1. Positioning system structure of underground coal mine personnel
系统架构 功能 硬件 硬件层 采集定位信息 人员标志卡、
UWB定位基站网络层 数据传输 光纤、交换机等 应用层 人员定位、车辆定位、
人员管理、紧急报警、智能巡检等地面计算平台、
车载智能计算与控制平台
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表 2 我国运营商5G主力频段
Table 2. Main frequency band of 5G in China
运营商 频率范围/MHz 带宽/MHz 频段 中国移动 2 515~2 675 160 n41 4 800~4 900 100 n79 中国广电 4 900~4 960 60 n79 703~733/758~788 2×30 n28 中国电信/
中国联通/
中国广电3 300~3 400 100 n78 中国电信 3 400~3 500 100 n78 中国联通 3 500~3 600 100 n78
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表 3 算法定位结果及误差
Table 3. Algorithm positioning results and error
cm 真实位置坐标 卡尔曼滤波 加权最小二乘 惯导+卡尔曼滤波 位置坐标 欧氏距离 位置坐标 欧氏距离 位置坐标 欧氏距离 （984，1121） （1023，1081） 55.865 91 （1071，1143） 89.738 51 （981，1110） 11.401 75 （859，607） （845，651） 46.173 59 （889，590） 34.481 88 （829，626） 35.510 56 （1135，1498） （1156，1454） 48.754 49 （1156，1533） 40.816 66 （1119，1509） 19.416 49 （1063，1472） （1072，1486） 16.643 32 （1061，1433） 39.051 25 （1068，1471） 5.099 02 （1333，1249） （1308，1300） 56.797 89 （1295，1290） 55.901 70 （1356，1265） 28.017 85 （577，847） （661，771） 113.27 84 （640，752） 113.991 20 （559，838） 20.124 61 （899，1107） （815，1011） 127.561 7 （922，1007） 102.610 90 （926，1086） 34.205 26 （1394，373） （1446，373） 52.000 0 （1489，409） 101.592 30 （1406，368） 13.00000 （900，597） （957，546） 76.485 29 （882，678） 82.975 90 （890，617） 22.360 68 （614，1219） （663，1206） 50.695 17 （688，1258） 83.648 07 （595，1221） 19.104 97 （1005，1030） （1015，960） 70.710 68 （1053，934） 107.331 30 （979，1027） 26.172 50 （851，1176） （776，1130） 87.982 95 （801，1243） 83.600 24 （827，1155） 31.890 44
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表 4 x轴真实位置坐标与定位位置坐标的定位误差
Table 4. Location error between the true position coordinates and position position coordinates of the x-axis
cm 标签1 标签2 标签3 $ {x_1} $ $ {x_2} $ E1 $ {x_1} $ $ {x_2} $ E1 $ {x_1} $ $ {x_2} $ E1 677 647 30 989 973 16 1 207 1 186 21 1 194 1 197 3 407 429 22 1 320 1 319 1 821 844 23 1 225 1 243 18 921 947 26 1 076 1 044 32 1 331 1 326 5 1 214 1 176 38 570 584 14 858 841 17 482 513 31 1 469 1 446 23 755 746 9 607 644 37 499 469 30 797 813 16 460 425 35 1 077 1 045 32 717 692 25 1 257 1 264 7 1 463 1 502 39 544 509 35 602 634 32 497 528 31 727 741 14 1 463 1 429 34 702 718 16 1 303 1 282 21 499 495 4 327 363 36 887 868 19 1335 1 343 8
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表 5 y轴真实位置坐标与定位位置坐标的定位误差
Table 5. Location error between the true position coordinates and position position coordinates ofthe y-axis
cm 标签1 标签2 标签3 $ {y_1} $ $ {y_2} $ $ {E_2} $ $ {y_1} $ $ {y_2} $ $ {E_2} $ $ {y_1} $ $ {y_2} $ $ {E_2} $ 893 918 25 1432 1 447 15 1 095 1 085 10 1 429 1 412 17 1 204 1 199 5 933 918 15 1 319 1 322 3 1 170 1 151 19 1 105 1 112 7 814 839 25 733 751 18 1 231 1 202 29 487 524 37 969 958 11 427 443 16 489 451 38 813 820 7 1 334 1 330 4 658 692 34 639 609 30 1 255 1 241 14 653 621 32 1 045 1 037 8 783 778 5 738 742 4 563 558 5 1 158 1 173 15 1 188 1 181 7 574 604 30 1 142 1 169 27 353 313 40 321 284 37 620 638 18 732 706 26 1 331 1323 8 1 259 1 239 20
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