Research on positioning algorithm of underground personnel based on UWB
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摘要: 针对井下高实时性、高精度的人员定位需求，研究了基于超宽带（UWB）的井下人员定位算法。采用双边双向测距（DS−TWR）方式测量定位基站与定位标签的距离，该方式不需要定位基站与定位标签系统时钟同步，从源头上提高了定位精度。根据测距信息，采用加权最小二乘（WLS）算法和CHAN两种位置解算算法估算定位标签的坐标，通过静态实验和动态实验对2种算法的性能进行对比分析，并通过均方根误差和误差累计分布函数（CDF）综合评估定位精度。实验结果表明：静态实验时，CHAN算法和WLS算法的均方根误差分别为5.878 6，8.007 4 cm，CHAN算法的均方根误差比WLS算法低26.59%；动态实验时，CHAN算法和WLS算法的均方根误差分别为12.292 3，21.180 9 cm，CHAN算法的均方根误差比WLS算法低41.97%；CHAN算法的定位精度高于WLS算法，更加适用于煤矿井下人员定位。Abstract: Aiming at the requirement of high real-time and high precision personnel positioning in underground mine, the positioning algorithm of underground personnel based on ultra wide band (UWB) is studied. The double-sided two-way ranging (DS-TWR) mode is adopted to measure the distance between the positioning base station and the positioning tag. This mode does not need the clock synchronization of the positioning base station and the positioning tag system. Therefore, the positioning precision is improved from the source. According to the ranging information, the weighted least squares (WLS) algorithm and CHAN algorithm are used to estimate the coordinates of the positioning tag. The performance of the two algorithms is compared and analyzed through static and dynamic experiments. The positioning precision is comprehensively evaluated through the root mean square error and the cumulative distribution function (CDF) of the error. The experimental results show that in static experiment, the root mean square errors of CHAN algorithm and WLS algorithm are 5.878 6 cm and 8.007 4 cm respectively. The root mean square error of CHAN algorithm is 26.59% lower than that of WLS algorithm. In dynamic experiment, the root mean square errors of CHAN algorithm and WLS algorithm are 12.2923 cm and 21.1809 cm respectively. The root mean square error of CHAN algorithm is 41.97% lower than that of WLS algorithm. The positioning precision of CHAN algorithm is higher than that of WLS algorithm. And CHAN algorithm is more suitable for underground personnel positioning in coal mines.
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图 2 实验环境与自制定位设备
Figure 2. Experimental environment and self-made positioning equipment
图 3 CHAN与WLS算法定位结果（静态实验）
Figure 3. Positioning results of CHAN and WLS algorithms (static experiment)
图 4 CHAN与WLS算法误差对比（静态实验）
Figure 4. Comparison of errors between CHAN and WLS algorithms(static experiment)
图 5 CHAN与WLS算法的误差CDF曲线（静态实验）
Figure 5. Error CDF curves of CHAN and WLS algorithms (static experiment)
图 6 CHAN与WLS算法定位结果（动态实验）
Figure 6. Positioning results of CHAN and WLS algorithms (dynamic experiment)
图 7 CHAN与WLS算法误差对比（动态实验）
Figure 7. Comparison of errors between CHAN and WLS algorithms(dynamic experiment)
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[1] 王龙康,李祥春,李安金,等. 我国煤矿安全生产现状分析及改善措施[J]. 中国煤炭,2016,42(9):96-100. doi: 10.3969/j.issn.1006-530X.2016.09.023WANG Longkang,LI Xiangchun,LI Anjin,et al. Analysis and improvement measures on current situation of coal mine safety production in China[J]. China Coal,2016,42(9):96-100. doi: 10.3969/j.issn.1006-530X.2016.09.023 [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] BIANCHI V,CIAMPOLINI P,MUNARI I D. RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes[J]. IEEE Transactions on Instrumentation and Measurement,2019,68(2):566-575. doi: 10.1109/TIM.2018.2851675 [4] VU-HOANG L, NGUYEN-MANH H, PHAN-DUY C, et al. A new technique to enhance accuracy of WLAN fingerprinting based indoor positioning system[C]//IEEE Fifth International Conference on Communications and Electronics, Danang, 2014. [5] CHEN X, WANG Z J. Reliable indoor location sensing technique using active RFID[C]//The 2nd International Conference on Industrial Mechatronics and Automation, Wuhan, 2010. [6] 刘鹏媛. 基于UWB的高速弹丸定距关键技术研究及实现[D]. 太原: 中北大学, 2020.LIU Pengyuan. Research and implementation of the key technology of high speed projectile distance determination based on UWB[D]. Taiyuan: North University of China, 2020. [7] 严嘉祺. 基于UWB的室内定位系统的算法与误差分析[D]. 哈尔滨: 哈尔滨工业大学, 2020.YAN Jiaqi. Algorithm and error analysis of indoor positioning system based on UWB[D]. Harbin: Harbin Institute of Technology, 2020. [8] 陈思远,尹栋,牛轶峰. 基于UWB的SS−TWR改进方法研究与实现[J]. 计算机应用研究,2021,38(11):3398-3402.CHEN Siyuan,YIN Dong,NIU Yifeng. Research and implementation of improved SS-TWR method based on UWB[J]. Application Research of Computers,2021,38(11):3398-3402. [9] SHULE W,ALMANSA C M,QUERALTA J P,et al. UWB-based localization for multi-UAV systems and collaborative heterogeneous multi-robot systems[J]. Procedia Computer Science,2020,175:357-364. doi: 10.1016/j.procs.2020.07.051 [10] WANG Gang,CAI Shu,LI Youming,et al. A bias-reduced nonlinear WLS method for TDOA/FDOA-based source localization[J]. IEEE Transactions on Vehicular Technology,2016,65(10):8603-8615. doi: 10.1109/TVT.2015.2508501 [11] LI Aiguo, LUAN Fuzeng. An improved localization algorithm based on CHAN with high positioning accuracy in NLOS-WGN environment[C]//The 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, 2018: 332-335. [12] 刘怡佳. 非视距环境下的UWB室内定位技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.LIU Yijia. Research on UWB indoor positioning technology in non-line-of-sight environment[D]. Harbin: Harbin Institute of Technology, 2020. [13] 顾慧东. 基于UWB的室内测距与定位系统[D]. 南京: 南京邮电大学, 2020.GU Huidong. Indoor ranging and positioning system based on UWB[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2020. -
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