运动分类步频调节的微机电惯性测量单元室内行人航迹推算

doi:10.3969/j.issn.1007-2861.2014.01.018

Journal of Shanghai University（Natural Science Edition） ›› 2014, Vol. 20 ›› Issue (5): 612-623.doi: 10.3969/j.issn.1007-2861.2014.01.018

• Communication and Information Engineering • Previous Articles Next Articles

Micro-Electro-Mechanical System-Inertial Measurement Unit Indoor Pedestrian Dead Reckoning Based on Motion Classification and Step Frequency Adjustment

LI Ruo-han1, ZHANG Jin-yi1,2,3, XU De-zheng2, CHEN Xing-xiu1, XU Qin-le2

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China; 2. Microelectronic Research and Development Center, Shanghai University, Shanghai 200072, China; 3. Key Laboratory of Advanced Displays and System Application, Shanghai University,
Shanghai 200072, China

Received:2014-01-02 Online:2014-10-30 Published:2014-10-30

Abstract

Abstract: As a new navigation method, pedestrian dead reckoning (PDR) has attracted much attention because it is less susceptible to environmental factors. To solve the indoor PDR problem, data of a micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) are collected and analyzed. A step detection algorithm is developed for motion classification and interval symmetry, and step length estimation model is established for step frequency adjustment. Thus a MEMS-IMU indoor PDR based on the motion classification and step frequency adjustment is constructed to realize accurate positioning. For different individuals, personalized step estimation model parameters are used to improve the positioning performance. Experimental results show that, the indoor PDR based on motion classification and step frequency adjustment reduces positioning error by 32.6% as compared to a traditional method using peak detection and a nonlinear model, achieving high positioning accuracy without resorting to any other positioning techniques.

Key words: indoor pedestrian dead reckoning (PDR), micro-electro-mechanical systeminertial measurement unit (MEMS-IMU), motion classification, step frequency, step length

CLC Number:

TP 273

LI Ruo-han1, ZHANG Jin-yi1,2,3, XU De-zheng2, CHEN Xing-xiu1, XU Qin-le2. Micro-Electro-Mechanical System-Inertial Measurement Unit Indoor Pedestrian Dead Reckoning Based on Motion Classification and Step Frequency Adjustment[J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(5): 612-623.

References

[1] Zhang S, Xiong Y, Ma J, et al. Indoor location based on independent sensors and WIFI [C]//International Conference on Computer Science and Network Technology. 2011: 2640-2643.

[2] Jimenez R A R, Seco G F, Prieto H J C, et al. Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements [J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(1): 178-189.

[3] Pratama A R, Widyawan H R. Smartphone-based pedestrian dead reckoning as an indoor positioning system [C]//International Conference on System Engineering and Technology. 2012: 1-6.

[4] Wang J S, Lin C W, Yang Y T C, et al. Walking pattern classification and walking distance estimation algorithms using gait phase information [J]. IEEE Transactions on Biomedical

Engineering, 2012, 59(10): 2884-2892.

[5] 杨清, 陈岭, 陈根才. 基于单加速度传感器的行走距离估计[J]. 浙江大学学报: 工学版, 2010, 44(9): 1681-1686.

[6] Zhang R, Bannoura A, Hoflinger F, et al. Indoor localization using a smart phone [C]//Sensors Applications Symposium. 2013: 38-42.

[7] Shin B, Lee J H, Lee H, et al. Indoor 3D pedestrian tracking algorithm based on PDR using smartphone [C]//International Conference on Control, Automation and Systems. 2012: 1442-1445.

[8] Chen W, Chen R Z, Chen Y W, et al. An effective pedestrian dead reckoning algorithm using a unified heading error model [C]//Position Location and Navigation Symposium. 2010: 340-347.

[9] Correa A, Morell A, Barcelo M, et al. Navigation system for elderly care applications based on wireless sensor networks [C]//20th European Signal Processing Conference. 2012: 210-214.

[10] 李昊, 简方梁. 人群行走荷载作用下的人致结构振动[J]. 华南理工大学学报: 自然科学版, 2010, 38(4): 125-130.

[11] Lee S, Kim B, Kim H, et al. Inertial sensor-based indoor pedestrian localization with minimum 802.15.4a configuration [J]. IEEE Transactions on Industrial Informatics, 2011, 7(3): 455-466.

[12] Kim Y K, Choi S H, Kim H W, et al. Performance improvement and height estimation of pedestrian dead-reckoning system using a low cost MEMS sensor [C]//International Conference on Control, Automation and Systems. 2012: 1655-1660.

Micro-Electro-Mechanical System-Inertial Measurement Unit Indoor Pedestrian Dead Reckoning Based on Motion Classification and Step Frequency Adjustment

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