Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T19:48:41.276Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved Heuristic Drift Elimination Algorithm Optimisation Based on a Smartphone Gyroscope

Published online by Cambridge University Press:  11 November 2019

Ying Guo ,
Xianlei Ji ,
Jinyun Guo ,
Jie Zhen ,
Ying Xu ,
Qinghua Liu  and
Yuxi Sun
Ying Guo
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China)
Xianlei Ji*
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China) (Guangzhou South Satellite Navigation Instrument Co., Ltd, Guangzhou510663, People's Republic of China)
Jinyun Guo
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China)
Jie Zhen
Affiliation:
(Chinese Academy of Surveying and Mapping, Beijing100830, People's Republic of China)
Ying Xu
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China)
Qinghua Liu
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China)
Yuxi Sun
Affiliation:
(College of Geomatics, Shandong University of Science and Technology, Qingdao266590, People's Republic of China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Heading errors caused by gyroscope drift affect the positioning precision of pedestrian dead reckoning, and these errors are even greater for smartphone-based reckoning. In this study, an optimised improved heuristic drift elimination (O-iHDE) method is proposed to correct the heading errors on a smartphone gyroscope. Based on an analysis of the improved heuristic drift elimination (iHDE) and enhanced improved heuristic drift elimination (E-iHDE) algorithms, the quaternion method is used to update the attitude and angle threshold judgement conditions, and a method for correcting the quaternion is added to eliminate the heading errors caused by random gyro errors. The analysis of multiple sets of experiments shows that the new method improves the ability to discern and correct the walking route, and the heading accuracy is improved by more than 90%, which extends the effective operation time of pedestrian dead reckoning positioning based on the step-by-step system.

Keywords

Smartphone GyroscopeOptimised Improved Heuristic Drift Elimination (O-iHDE)QuaternionHeading Error
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 581 - 598
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2010). Aiding MEMS IMU with Building Heading for Indoor Pedestrian Navigation. Ubiquitous Positioning Indoor Navigation and Location Based Service, 14–15 October, Kirkkonummi, Finland, 1–6.CrossRefGoogle Scholar
Abid, M., Renaudin, V., Aoustin, Y., Le-Carpentier, E. and Robert, T. (2017). Walking gait step length asymmetry induced by handheld device. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 2075–2083.CrossRefGoogle ScholarPubMed
Borenstein, J. and Ojeda, L. (2009). Heuristic reduction of gyro drift in IMU-based personnel tracking systems. Journal of Navigation, 62(1), 41–58.CrossRefGoogle Scholar
Borenstein, J. and Ojeda, L. (2010). Heuristic drift elimination for personnel tracking systems. Journal of Navigation, 63(4), 591–606.CrossRefGoogle Scholar
Brigante, C. M. N., Abbate, N., Basile, A., Faulisi, A. C. and Sessa, S. (2011). Towards miniaturization of a MEMS-based wearable motion capture system. IEEE Transactions on Industrial Electronics, 58(8), 3234–3241.CrossRefGoogle Scholar
Chen, G. L., Zhang, Y. Z., Wang, Y. J. and Meng, X. L. (2015). Unscented Kalman filter algorithm for WiFi-PDR integrated indoor positioning. Acta Geodaetica et Cartographica Sinica, 44(12), 1314–1321.Google Scholar
Chen, L. B., Li, S. J. and Pan, G. (2015). Smartphone: Pervasive sensing and applications. Chinese Journal of Computers, 38(2), 423–438.Google Scholar
Chen, R. Z. and Chen, L. (2017). Indoor positioning with smartphones: The state-of-the-art and the challenges. Acta Geodaetica et Cartographica Sinica, 46(10), 1316–1326.Google Scholar
Davidson, P. and Piche, R. (2017). A survey of selected indoor positioning methods for smartphones. IEEE Communications Surveys & Tutorials, 19(2), 1347–1370.CrossRefGoogle Scholar
Diez, L. E., Bahillo, A., Bataineh, S., Masegosa, A. D. and Perallos, A. (2016). Enhancing Improved Heuristic Drift Elimination for Wrist-Worn PDR Systems in Buildings. Vehicular Technology Conference. Montreal, Canada, 1–5.CrossRefGoogle Scholar
Guo, Y., Ji, X. L., Liu, Q. H., Li, G. Z. and Xu, Y. (2017a). Heading correction algorithm based on mobile phone gyroscope. Journal of Chinese Inertial Technology, 25(6), 719–724.Google Scholar
Guo, Y., Liu, Q. H., Ji, X. L., Li, G. Z. and Wang, S. L. (2017b). Pedestrian gait analysis based on mobile phone accelerometer. Journal of Chinese Inertial Technology, 25(6), 708–712.Google Scholar
Guo, S., Xiong, H. and Zheng, X. (2017c). A novel semantic matching method for indoor trajectory tracking. International Journal of Geo-Information, 6(7), 197.CrossRefGoogle Scholar
Harle, R. (2013). A survey of indoor inertial positioning systems for pedestrians. IEEE Communications Surveys & Tutorials, 15(3), 1281–1293.CrossRefGoogle Scholar
Hu, A. D., Wang, J., Wang, Y. J., Liu, C. Y., Tan, X. L. and Li, Z. K. (2016). A fusion positioning for PDR and WiFi based on fading adaptive weighted EKF. Geomatics and Information Science of Wuhan University, 41(11), 1556–1562.Google Scholar
Jiménez, A. R., Seco, F., Prieto, C. and Guevara, J. (2009). A Comparison of Pedestrian Dead-Reckoning Algorithms Using a Low-Cost MEMS IMU. IEEE International Symposium on Intelligent Signal Processing. Budapest, Hungary, 37–42.CrossRefGoogle Scholar
Jiménez, A. R., Seco, F., Zampella, F., Prieto, J. C. and Guevara, J. (2011). Improved Heuristic Drift Elimination (iHDE) for Pedestrian Navigation in Complex Buildings. International Conference on Indoor Positioning and Indoor Navigation. IEEE, 21–23 September, Guimaraes, Portugal, 1–8.CrossRefGoogle Scholar
Ju, H. J., Min, S. L., Chan, G. P., Lee, S. and Park, S. (2015). Advanced Heuristic Drift Elimination for Indoor Pedestrian Navigation. International Conference on Indoor Positioning and Indoor Navigation. Busan, South Korea, 729–732.CrossRefGoogle Scholar
Kang, W. and Han, Y. (2015). SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sensors Journal, 15(5), 2906–2916.CrossRefGoogle Scholar
Liu, D., Pei, L., Qian, J., Wang, L. and Liu, P. L. (2017). A Novel Heading Estimation Algorithm for Pedestrian Using a Smartphone Without Attitude Constraints. Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services. Shanghai, 29–37.Google Scholar
Lou, X. Z., Zhou, L. Y., Ye, M. Z., Jia, Y. Z. and Jin, N. (2015). Researching on indoor pedestrian trajectory based on angle HDE algorithm. Chinese Journal of Sensors and Actuators, 28(4), 598–602.Google Scholar
Torres-Sospedra, J., Jiménez, A. R., Knauth, S., Moreira, A., Beer, Y., Fetzer, T., Ta, V. C., Montoliu, R., Seco, F., MendozaSilva, G. M., Belmonte, O., Koukofikis, A., Nicolau, M. J., Costa, A., Meneses, F., Ebner, F., Deinzer, F., Vaufreydaz, D., Dao, T. K. and Castelli, E. (2017). The smartphone-based offline indoor location competition at IPIN 2016: Analysis and future work. Sensors, 17(3), 557.CrossRefGoogle ScholarPubMed
Zeng, Q. J., Liu, H. T. and Zhang, M. (2016). Analysis and processing on random drift of gyroscope based on HDR. Journal of Jiangsu University (Natural Science Edition), 37(3), 332–336.Google Scholar
Zhang, L. Q., Su, Z. and Li, Q. (2017). Pedestrian navigation framework iIEZ+ based on quasi-static magnetic field detection. Chinese Journal of Sensors and Actuators, 30(4), 542–549.Google Scholar
Zhang, J. M., Xiu, C. D., Yang, W. and Yang, D. K. (2017). Adaptive threshold zero velocity update algorithm under multi-movement patterns. Journal of Beijing University of Aeronautics and Astronautics, 44(03), 636–644.Google Scholar
Zhao, H., Li, Q. and Li, C. (2016). A heading correction algorithm based on the main direction for pedestrian navigation. Application of Electronic Technique, 42(11), 108–111.Google Scholar
Zheng, W. and Peng, G. (2016). 3D pedestrian trajectory tracking based on inertial/magnetic sensors. Robot, 38(4), 444–450.Google Scholar