A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration

doi:10.13190/j.jbupt.2020-165

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 53-60.doi: 10.13190/j.jbupt.2020-165

Previous Articles Next Articles

A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration

BAI Yan-ru¹, LUO Hai-yong², CAO Cheng-lin³, WANG Qu³, XIONG Hao³

1. School of Advanced Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-09-03 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: Currently commonly-used micro electro mechanical system magnetic sensors have time-varying soft magnetic and hard magnetic errors, which seriously affect the performance of geomagnetic-based heading estimation and geomagnetic matching positioning algorithms. Using the opportunistic natural rotation of pedestrians during normal walking, the gyroscope is used to sense the small-scale attitude changes of magnetic sensors, and a nonlinear objective cost function based on residual dynamic weighting is constructed, which contains multiple optimal magnetic observation pairs. The cuckoo nonlinear optimization algorithm with global optimal solution is used to dynamically estimate the soft and hard magnetic errors online. The average heading error of 3.09 degrees can be reduced by using the proposed magnetic calibration algorithm, and the relative error of the navigation estimation is 2.09% when tested on the pedestrian dead reckoning algorithm.

Key words: pedestrian dead reckoning, indoor positioning, magnetometer calibration, pedestrian tracking

CLC Number:

TP212.9

BAI Yan-ru, LUO Hai-yong, CAO Cheng-lin, WANG Qu, XIONG Hao. A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 53-60.

References

[1] 张平, 陈昊. 面向5G的定位技术研究综述[J]. 北京邮电大学学报, 2018, 41(5):1-12. Zhang Ping, Chen Hao. A survey of positioning technology for 5G[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(5):1-12.
[2] Shao Wenhua, Luo Haiyong, Zhao Fang, et al. Toward improving indoor magnetic field-based positioning system using pedestrian motion models[J]. International Journal of Distributed Sensor Networks, 2018, 14(9):1-20.
[3] Shao Wenhua, Zhao Fang, Wang Cong, et al. Location fingerprint extraction for magnetic field magnitude based indoor positioning[J]. Journal of Sensors, 2016, 2016:1-16.
[4] Wang Qu, Luo Haiyong, Men Aidong, et al. An infrastructure-free indoor localization algorithm for smartphones[J]. Sensors, 2018, 18(10):3317-3340.
[5] Li Xiang, Song Baiqi, Wang Yongjun, et al. Calibration and alignment of tri-axial magnetometers for attitude determination[J]. IEEE Sensors Journal, 2018, 18(18):7399-7406.
[6] Deng Yang, Zheng You, Bin Li, et al. Complete tri-axis magnetometer calibration with a gyro auxiliary[J]. Sensors, 2017, 17(6):1223-1243.
[7] Foster C C, Elkaim G H. Extension of a two-step calibration methodology to include nonorthogonal sensor axes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3):1070-1078.
[8] 刘宇, 陈永炜, 路永乐, 等. 基于最小二乘法的磁力计误差补偿与校准[J]. 导航定位与授时, 2018, 5(1):17-21. Liu Yu, Chen Yongwei, Lu Yongle, et al. Research on error compensation and calibration of magnetometer based on least square method[J]. Navigation Positioning and Timing, 2018, 5(1):17-21.
[9] Cao Guocan, Xu Xiang, Xu Dacheng. Real-Time calibration of magnetometers using the RLS/ML algorithm[J]. Sensors, 2020, 20(2):535-549.
[10] Wu Yuanxin, Shi Wei. On calibration of three-axis magnetometer[J]. IEEE Sensors Journal, 2015, 15(11):6424-6431.
[11] Fang Jiancheng, Sun Hongwei, Cao Juanjuan, et al. A novel calibration method of magnetic compass based on ellipsoid fitting[J]. IEEE Transactions on Instrumentation & Measurement, 2011, 60(6):2053-2061.
[12] Riwanto B A, Tikka T, Kestilä A, et al. Particle swarm optimization with rotation axis fitting for magnetometer calibration[J]. IEEE Transactions on Aerospace & Electronic Systems, 2017, 53(2):1009-1022.
[13] Yu Hairong, Ye Lin, Guo Ying, et al. An innovative 9-parameter magnetic calibration method using local magnetic inclination and calibrated acceleration value[J]. IEEE Sensors Journal, 2020, 20(19):11275-11282.
[14] Zhu Maoran, Wu Yuanxin, Yu Wenxian. An efficient method for gyroscope-aided full magnetometer calibration[J]. IEEE Sensors Journal, 2019, 19(15):6355-6361.
[15] Spielvogel A R, Whitcomb L L. A stable adaptive observer for hard-iron and soft-iron bias calibration and compensation for two-axis magnetometers:theory and experimental evaluation[J]. IEEE Robotics and Automation Letters, 2020, 5(2):1295-1302.
[16] Wang Qu, Ye Langlang, Luo Haiyong, et al. Pedestrian stride-length estimation based on LSTM and denoising autoencoders[J]. Sensors, 2019, 19(4):840-862.
[17] Kang W, Han Y. SmartPDR:smartphone-based pedestrian dead reckoning for indoor localization[J]. IEEE Sensors Journal, 2015, 15(5):2906-2916.
[18] Deng Zhian, Liu Xin, Qu Zhiyu, et al. Robust heading estimation for indoor pedestrian navigation using unconstrained smartphones[J]. Wireless Communications and Mobile Computing, 2018, 2018(6):1-11.

A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	REN Xiao-qi, OWN Chung-ming. RSS Missing Value Estimation with Generative Adversarial Networks Model [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 26-31.
[2]	ZHU Ming-qiang, HOU Jian-jun, LIU Ying, SU Jun-feng. Indoor Localization Estimation Based on Scaled Iterated Unscented Kalman Filter [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2013, 36(4): 65-70.
[3]	ZHAO Jun-hui, ZHANG Xue-xue, ZENG Long-ji. An Approach to Improving the Accuracy of the Indoor Positioning with NLOS Environment [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2012, 35(6): 38-43.