%0 Journal Article
%A CAO Cheng-lin
%A LUO Hai-yong
%A WANG Qu
%A XIONG Hao
%A BAI Yan-ru
%T A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration
%D 2021
%R 10.13190/j.jbupt.2020-165
%J Journal of Beijing University of Posts and Telecommunications
%P 53-60
%V 44
%N 3
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2020-165