Pedestrian Heading Estimation Methods Based on Multiple Phone Carrying Modes
Authors: 
Ying Guo, Hanshuo Liu, Jin Ye, Shengli Wang, Chenxi Duan Academic Editor: Juraj MachajAuthors Info & Claims
Ying Guo, Hanshuo Liu, Jin Ye, Shengli Wang, Chenxi Duan Academic Editor: Juraj MachajAuthors Info & ClaimsAbstract
The development of smartphone Micro-Electro-Mechanical Systems (MEMS) inertial sensors has provided opportunities to improve indoor navigation and positioning for location-based services. One area of indoor navigation research uses pedestrian dead reckoning (PDR) technology, in which the mobile phone must typically be held to the pedestrian’s chest. In this paper, we consider navigation in three other mobile phone carrying modes: “calling,” “pocket,” and “swinging.” For the calling mode, in which the pedestrian holds the phone to their face, the rotation matrix method is used to convert the phone’s gyroscope data from the calling state to the holding state, allowing calculation of the stable pedestrian forward direction. For a phone carried in a pedestrian’s trouser pocket, a heading complementary equation is established based on principal component analysis and rotation approach methods. In this case, the pedestrian heading is calculated by determining a subset of data that avoid 180° directional ambiguity and improve the heading accuracy. For the swinging mode, a heading capture method is used to obtain the heading of the lowest point of the pedestrian’s arm swing as they hold the phone. The direction of travel is then determined by successively adding the heading offsets each time the arm droops. Experimental analysis shows that 95% of the heading errors of the above three methods are less than 5.81°, 10.73°, and 9.22°, respectively. These results present better heading accuracy and reliability.
References
[1]
X. He, R. Jin, and H. Dai, “Leveraging spatial diversity for privacy-aware location-based services in mobile networks,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 6, pp. 1524–1534, 2018.
[2]
L. Zhang, J. Liu, J. Lai, and Z. Xiong, “Performance analysis of adaptive neuro fuzzy inference system control for MEMS navigation system,” Mathematical Problems in Engineering, vol. 2014, 7 pages, 2014.
[3]
E. D. Kaplan and C. Hegarty, Understanding GPS Principles and Applications, Artech House, Norwood, MA, USA, 2005.
[4]
W. Elloumi, A. Latoui, R. Canals, A. Chetouani, and S. Treuillet, “Indoor pedestrian localization with a smartphone: a comparison of inertial and vision-based methods,” IEEE Sensors Journal, vol. 16, no. 13, pp. 5376–5388, 2016.
[5]
F. Hu, Z. Zhu, and J. Zhang, “Mobile panoramic vision for assisting the blind via indexing and localization,” in Proceedings of the Computer Vision-ECCV 2014 Workshops, pp. 600–614, Zurich, Switzerland, September 2015.
[6]
L.-F. Shi, Y. Wang, G.-x. Liu, S. Chen, Y.-L. Zhao, and Y.-F. Shi, “A fusion algorithm of indoor positioning based on PDR and RSS fingerprint,” IEEE Sensors Journal, vol. 18, no. 23, pp. 9691–9698, 2018.
[7]
Z. Chen, H. Zou, H. Jiang, Q. Zhu, Y. Soh, and L. Xie, “Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization,” Sensors, vol. 15, no. 1, pp. 715–732, 2015.
[8]
Q. Zeng, J. Wang, Q. Meng, X. Zhang, and S. Zeng, “Seamless pedestrian navigation methodology optimized for indoor/outdoor detection,” IEEE Sensors Journal, vol. 18, no. 1, pp. 363–374, 2018.
[9]
A. Manos, I. Klein, and T. Hazan, “Gravity-based methods for heading computation in pedestrian dead reckoning,” Sensors, vol. 19, no. 5, p. 1170, 2019.
[10]
J. Zwiener, A. Lorenz, and R. Jager, “Algorithm and system development for seamless out-/indoor navigation with lowcost GNSS and foot-mounted MEMS sensor,” in Proceedings of the International Scientific Congress and Exhibition Interexpo Geo-Siberia, pp. 83–97, Novosibirsk, Russia, April 2014.
[11]
J. O. Nilsson, D. Zachariah, I. Skog, and P. Handel, “Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging,” EURASIP Journal on Advances in Signal Processing, vol. 2013, 17 pages, 2013.
[12]
S. Qiu, Z. Wang, H. Zhao, K. Qin, Z. Li, and H. Hu, “Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion,” Information Fusion, vol. 39, pp. 108–119, 2018.
[13]
L. Zheng, W. Zhou, W. Tang, X. Zheng, A. Peng, and H. Zheng, “A 3D indoor positioning system based on low-cost MEMS sensors,” Simulation Modelling Practice and Theory, vol. 65, pp. 45–56, 2016.
[14]
L. Xu, Z. Xiong, J. Liu, Z. Wang, and Y. Ding, “A novel pedestrian dead reckoning algorithm for multi-mode recognition based on smartphones,” Remote Sensing, vol. 11, no. 3, p. 294, 2019.
[15]
Q. Tian, Z. Salcic, K. I.-K. Wang, and Y. Pan, “A multi-mode dead reckoning system for pedestrian tracking using smartphones,” IEEE Sensors Journal, vol. 16, no. 7, pp. 2079–2093, 2016.
[16]
J.-S. Lee and S.-M. Huang, “An experimental heuristic approach to multi-pose pedestrian dead reckoning without using magnetometers for indoor localization,” IEEE Sensors Journal, vol. 19, no. 20, pp. 9532–9542, 2019.
[17]
U. Steinhoff and B. Schiele, “Dead reckoning from the pocket-an experimental study,” in Proceedings of the 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 162–170, Mannheim, Germany, March 2010.
[18]
Z. A. Deng, G. Wang, Y. Hu, and Y. Cui, “Carrying position independent user heading estimation for indoor pedestrian navigation with smartphones,” Sensors, vol. 16, no. 5, 2016.
[19]
B. Wang, X. Liu, B. Yu, R. Jia, and X. Gan, “Pedestrian dead reckoning based on motion mode recognition using a smartphone,” Sensors, vol. 18, no. 6, 2018.
[20]
L. Zheng, X. Zhan, X. Zhang, S. Wang, and W. Yuan, “Heading estimation for multimode pedestrian dead reckoning,” IEEE Sensors Journal, vol. 20, no. 15, pp. 8731–8739, 2020.
[21]
Y. Guo, H. Liu, J. Ye, and F. Yuan, “Pedestrian heading correction method based on AHDE and mobile phone gyroscope,” Journal of Chinese Inertial Technology, vol. 29, no. 1, pp. 8–15, 2021.
[22]
Y. Guo, X. Ji, Q. Liu, G. Li, and Y. Xu, “Heading correction algorithm based on mobile phone gyroscope,” Journal of Chinese Inertial Technology, vol. 25, no. 6, pp. 719–724, 2017.
[23]
T. Akenine-Moller, T. Moller, and E. Haines, Real-Time Rendering, A. K. Peters, Natick, MA, USA, 2002.
[24]
J. Ma, Research of a Smartphone Based Indoor Pedestrian Dead Reckoning System, Shanghai Jiao Tong University, Shanghai, China, 2014.
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Published: 01 January 2021
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