融合UWB与INS的消防员室内定位与NLOS检测算法

doi:10.19678/j.issn.1000-3428.0059311

摘要/Abstract

摘要： 融合超宽带（UWB）和惯性导航系统（INS）能够实现消防员室内精确定位。为实现UWB的非视距（NLOS）误差检测，设计一种双级EKF框架。该框架以松耦合形式实现UWB/INS的数据融合，通过INS获取的初始位置估计坐标以检测UWB测量值的NLOS误差，根据检测结果计算残差矩阵来动态调整融合滤波器的测量噪声矩阵，以达到缓解NLOS误差的目的。实验结果表明，与三角不等式原理检测算法和无NLOS检测的UWB/INS简单融合算法相比，所提NLOS检测算法具备良好的检测能力、较强的稳定性及较高的定位精度。

关键词: 超宽带, 惯性导航系统, 消防员定位, 非视距, 扩展卡尔曼滤波

Abstract: Accurate indoor positioning of firefighters can be implemented by using Ultra-Wideband(UWB) and Inertial Navigation System(INS), but Non-Line-of-Sight(NLOS) error detection and mitigation of UWB remain a problem.To this end, a two-stage Extended Kalman Filter(EKF) framework is designed to achieve UWB/INS data fusion in a loosely coupled manner.The estimated coordinates of the initial position are obtained by INS, and then used to detect the NLOS error of the UWB measurement value.Based on the detection result, the residual matrix is calculated to adjust the measurement noise matrix of the fusion filter dynamically, so the NLOS error can be alleviated.The proposed algorithm is tested and compared with the detection algorithm based on the triangle inequality theorem and the UWB/INS without NLOS detection.Results show that the proposed NLOS detection algorithm exhibits excellent detection performance and stability, and the fusion of UWB/INS can effectively improve the positioning accuracy.

Key words: Ultra-Wideband(UWB), Inertial Navigation System(INS), firefighters positioning, Non-Line-of-Sight(NLOS), extended Kalman filter

中图分类号:

TP391

杨刚, 朱士玲, 李强, 赵克松. 融合UWB与INS的消防员室内定位与NLOS检测算法[J]. 计算机工程, 2021, 47(9): 153-161.

YANG Gang, ZHU Shiling, LI Qiang, ZHAO Kesong. Indoor Firefighter Positioning and NLOS Detection Algorithm Based on UWB and INS[J]. Computer Engineering, 2021, 47(9): 153-161.

https://www.ecice06.com/CN/Y2021/V47/I9/153

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参考文献

[1] ZAFARI F, GKELIAS A, LEUNG K K, et al.A survey of indoor localization systems and technologies[J].IEEE Communications Surveys and Tutorials, 2019, 21(3):2568-2599.
[2] MAZHAR F, KHAN M G, SALLBERG B, et al.Precise indoor positioning using UWB:a review of methods, algorithms and implementations[J].Wireless Personal Communications, 2017, 97(3):4467-4491.
[3] KOJAKIAN V, GENON-CATALOT D, FOURTY N, et al.Firefighter indoor localization[C]//Proceedings of IEEE Radio and Antenna Days of the Indian Ocean.Washington D.C., USA:IEEE Press, 2019:1-2.
[4] ZHANG K, SHEN C, GAO Q, et al.Research on similarity metric distance algorithm for indoor and outdoor firefighting personnel precision wireless location system based on vague set on UWB[C]//Proceedings of IEEE International Conference on Communication Technology.Washington D.C., USA:IEEE Press, 2017:1162-1165.
[5] 王长强, 徐爱功, 隋心.超宽带双向到达时间测距的室内动态定位方法[J].测绘科学, 2017, 42(12):151-156. WANG C Q, XU A G, SUI X.Indoor dynamic positioning method for UWB two-way arrival time ranging[J].Surveying and Mapping Science, 2017, 42(12):151-156. (in Chinese)
[6] SCHROEDER J, GALLER S, KYAMAKYA K, et al.NLOS detection algorithms for ultra-wideband localization[C]//Proceedings of Workshop on Positioning, Navigation and Communication.Washington D.C., USA:IEEE Press, 2007:159-166.
[7] 杨刚, 李强, 韩路, 等.基于MEMS-MARG传感器的消防员室内定位算法[J].中国惯性技术学报, 2019, 27(5):598-604. YANG G, LI Q, HAN L, et al.Indoor positioning algorithm for firefighters based on MEMS-MARG sensor[J].Journal of Chinese Inertial Technology, 2019, 27(5):598-604.(in Chinese)
[8] LEE M, JU H, SONG J, et al.Kinematic model-based pedestrian dead reckoning for heading correction and lower body motion tracking[J].Sensors, 2015, 15(11):28129-28153.
[9] 华海亮, 关维国, 刘志建, 等.基于优化贝叶斯的室内WiFi与蓝牙融合定位算法[J].计算机工程, 2016, 42(11):114-119. HUA H L, GUAN W G, LIU Z J, ET al.Indoor WiFi and bluetooth fusion localization algorithm based on optimal Bayes[J].Computer Engineering, 2016, 42(11):114-119.(in Chinese)
[10] 刘庆, 关维国, 李顺康, 等.基于扩展Kalman滤波的室内WiFi-PDR融合定位算法[J].计算机工程, 2019, 45(4):66-71, 77. LIU Q, GUAN W G, LI S K, et al.Indoor WiFi-PDR fusion location algorithm based on extended Kalman filter[J].Computer Engineering, 2019, 45(4):66-71, 77.(in Chinese)
[11] 仪玉杰, 黄智刚, 苏雨.一种UWB与PDR融合的行人室内定位方法[J].导航定位学报, 2019, 7(3):38-43. YI Y J, HUANG Z G, SU Y.A method of pedestrian indoor positioning combined UWB with PDR[J].Journal of Navigation and Positioning, 2019, 7(3):38-43.(in Chinese)
[12] TIAN Q, KEVIN I, WANG K, et al.A low-cost INS and UWB fusion pedestrian tracking system[J].IEEE Sensors Journal, 2019, 19(10):3733-3740.
[13] 徐元, 陈熙源.面向室内行人的Range-only UWB/INS紧组合导航方法[J].仪器仪表学报, 2016, 37(9):2115-2121. XU Y, CHEN X Y.Range-only UWB/INS tightly-coupled integrated navigation method for indoor pedestrian[J].Chinese Journal of Scientific Instrument, 2016, 37(9):2115-2121.(in Chinese)
[14] TIAN Q, KEVIN I, WANG K, et al.An INS and UWB fusion approach with adaptive ranging error mitigation for pedestrian tracking[J].IEEE Sensors Journal, 2020, 20(8):4372-4381.
[15] WANG Y, LI X.The IMU/UWB fusion positioning algorithm based on a particle filter[J].ISPRS International Journal of Geo-Information, 2017, 6(8):235.
[16] TIAN Q, KEVIN I, WANG K, et al.A resetting approach for INS and UWB sensor fusion using particle filter for pedestrian tracking[J].IEEE Transactions on Instrumentation and Measurement, 2020, 69(8):5914-5921.
[17] FERREIRA A G, FERNANDES D, CATARINO A P, et al.A loose-coupled fusion of inertial and UWB assisted by a decision-making algorithm for localization of emergency responders[J].Electronics, 2019, 8(12):1463.
[18] XU Y, LI Y, AHN C K, et al.Seamless indoor pedestrian tracking by fusing INS and UWB measurements via LS-SVM assisted UFIR filter[J].Neurocomputing, 2020, 388:301-308.
[19] JUNG H, LIM J, SHIM J.IMU/UWB sensor fusion using moving average filter for Indoor positioning of mobile robot[J].Journal of Institute of Control, Robotics and Systems, 2020, 26(4):242-248.
[20] FAN Q, SUN B, SUN Y, et al.Performance enhancement of MEMS-based INS/UWB integration for indoor navigation applications[J].IEEE Sensors Journal, 2017, 17(10):3116-3130.
[21] ZENG Z, LIU S, WANG L.UWB/IMU integration approach with NLOS identification and mitigation[C]//Proceedings of Annual Conference on Information Sciences and Systems.Washington D.C., USA:IEEE Press, 2018:1-6.
[22] HOL J D, DIJKSTRA F, LUINGE H, et al.Tightly coupled UWB/IMU pose estimation[C]//Proceedings of IEEE International Conference on Ultra-wideband.Washington D.C., USA:IEEE Press, 2009:688-692.
[23] LI X, WANG Y, LIU D.Research on extended kalman filter and particle filter combinational algorithm in UWB and foot-mounted IMU fusion positioning[J].Mobile Information Systems, 2018, 19(3):1-17.

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