Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

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

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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. 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: 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: