基于多步态零速检测的MEMS IMU室内定位技术
Indoor Positioning Technology Based on MEMS IMU Using Multi-Step Zero Speed Detection
DOI: 10.12677/JSTA.2019.72004, PDF,   
作者: 刘晓东*, 田 爽, 粟 伟：重庆华渝电气集团有限公司，技术开发中心，重庆
关键词: MEMS惯性传感器；室内定位；零速区间；卡尔曼滤波；MEMS Inertial Sensor； Indoor Positioning； Zero Velocity Interval； Kalman Filter
摘要: 通过研究基于低成本MEMS/IMU的室内定位方法，针对传统单一阈值零速区间检测算法存在的不足，提出了一种适应于多步态的零速区间检测算法。低成本MEMS IMU固联在脚上，先根据传感器测量到的信息判断行人属于哪种运动状态，进而在相应状态的阈值条件下采用比力、比力方差、角速度三者相结合的方法进行零速区间检测，同时设计卡尔曼滤波器在静止时间段估计出系统的姿态误差、速度位置误差，并通过反馈校正对系统进行误差补偿。最后采用低成本MEMS IMU进行实验验证，结果表明定位精度可以保持在总行程的3%左右。
Abstract: An indoor positioning method based on low cost MEMS inertial sensor is studied. Aiming at the shortcomings of traditional single threshold zero speed interval detection algorithm, a new algo-rithm suitable for multi gait is proposed. The low cost inertial measurement unit (IMU) is fixed on the foot. Firstly, according to the output of the acceleration, motion state of the pedestrian can be determined. Then, under the threshold condition of the corresponding state, the zero speed interval detection is carried out by combining the three methods of specific force, specific force variance and angular velocity. At the same time, the Kalman filter is designed to estimate the system attitude error and the speed position error in the static time and the system is compensated by the feedback correction. Finally, the low cost MEMS IMU is used f to verify the experiment. The results show that the positioning accuracy can be maintained at about 3%.
文章引用：刘晓东, 田爽, 粟伟. 基于多步态零速检测的MEMS IMU室内定位技术[J]. 传感器技术与应用, 2019, 7(2): 28-38. https://doi.org/10.12677/JSTA.2019.72004

参考文献

[1] Jo, K., Chu, K. and Sunwoo, M. (2012) Interacting Multiple Model Filter-Based Sensor Fusion of GPS with In-Vehicle Sensors for Real-Time Vehicle Positioning. IEEE Transactions on Intelligent Transportation Systems, 13, 329-343.
[2] 刘成. LBS定位技术研究与发展现状[J]. 导航定位学报, 2013(1): 78-83.
[3] Foxlin, E. (2005) Pedestrian Tracking with Shoe-Mounted Inertial Sensors. IEEE Computer Graphics and Applications, 25, 38-46.
[4] Alvarez, J.C., López, A.M., González, R.C., et al. (2012) Pedestrian Dead Reckoning with Waist-Worn Inertial Sensors. Proceedings of Instrumentation and Measurement Technology Conference (12MTC), Graz, Austria, 13-16 May 2012, 24-27.
[5] Fourati, H. (2015) Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter. IEEE Transactions on Instrumentation Measurement, 64, 221-229. [Google Scholar] [CrossRef] 
[6] 张金亮, 秦永元, 梅春波. 基于MEMS惯性技术的鞋式个人导航系统[J]. 中国惯性技术学报, 2011, 19(3): 253-256.
[7] 王立兵, 杨松普, 罗巍, 等. 足部安装MEMS-IMU个人导航系统[J]. 中国惯性技术学报, 2016, 24(4): 460-463.
[8] 王普, 潘凯, 任明荣, 等. 基于伪自适应阈值零速检测法的室内个人导航系统[J]. 北京工业大学学报, 2015(9): 1308-1313.
[9] Bebek, O., Suster, M.A., Rajgopal, S., et al. (2010) Personal Navigation via Shoe Mounted Inertial Measurement Units. Proceedings of International Conference on Intelligent Robots and Systems (IROS), Taipei, 18-22 October 2010.
[10] Ramón, A., Ruiz, J., et al. (2012) Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements. Instrumentation and Measurement, 11, 178-189.
[11] Skog, I., Handel, P., Nilsson, J., et al. (2010) Zero-Velocity Detection—An Algorithm Evaluation. IEEE Transactions on Biomedical Engineering, 57, 2657-2666.
[12] Zhang, H., Yuan, W., Shen, Q., et al. (2015) A Handheld Inertial Pedestrian Navigation System with Accurate Step Modes and Device Poses Recognition. IEEE Sensors Journal, 15, 1421-1429. [Google Scholar] [CrossRef] 

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