Gyro Bias Estimation with Quasi-static Magnetic Field in Foot-mounted Pedestrian Dead Reckoning

Jae Hong Lee, Soyoung Park, Seoung Yun Cho, Chan Gook Park

Peer Reviewed

Abstract: In this paper, the quasi-static magnetic field (QSF) aided gyro bias estimation method with zero angular rate recognition is proposed. The integration approach (IA)-based PDR system is a method of estimating the user's position using an inertial sensor attached to a shoe. Since the structure is similar to that of INS, accurate gyro bias estimation is required to reduce the error. A reliable way to estimate the gyro bias is to average the signal at the initial standstill period. However, when it is necessary to operate the system in an emergency situation such as a first agent, it is not appropriate to maintain a long stop before mission for estimating gyro bias. QSF is a method of estimating the gyro bias even in walking, not at rest, using local magnetic vectors. However, the QSF detector only considers that the rate of the magnetic vector does not change. It may cause an error in the gyro bias. In order to improve the estimation performance, this paper proposed a detection method that considers the zero angular rate. The zero angular rate is detected by comparing the current gyro signal with gyro signal during the initial short stop period, and combined with the QSF detector. If this detector is taken into account, more accurate estimation is possible in gyro bias. The experimental results indicate that the proposed method improves the estimation performance even under walking conditions.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 765 - 769
Cite this article: Lee, Jae Hong, Park, Soyoung, Cho, Seoung Yun, Park, Chan Gook, "Gyro Bias Estimation with Quasi-static Magnetic Field in Foot-mounted Pedestrian Dead Reckoning," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 765-769. https://doi.org/10.1109/PLANS46316.2020.9110237
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