Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

Author:

Cui Xufei12,Li Yibing1,Wang Qiuying34ORCID,Karaim Malek2,Noureldin Aboelmagd25

Affiliation:

1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

2. School of Computing, Queen’s University, Kingston, ON, Canada

3. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

4. Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, Heilongjiang, China

5. Electrical and Computer Engineering Department, Royal Military College of Canada, Kingston, ON, Canada

Abstract

The integrated INS/magnetometer measurement is widely used in low-cost navigation systems. The integration has proven more effective in suppressing the divergence of heading than relying solely on a magnetometer because this is susceptible to local magnetic field interference, reducing heading accuracy. Magnetometers sense the local magnetic field that may be interfered by the nearby ferromagnetic material or strong electric currents. Hence, the magnetometer must be calibrated in the vehicle before use. When a magnetometer is installed near power components (engines, etc.), soft iron interference can be ignored. In the vehicle’s external environment, the time-varying hard iron interference can reach 100 times the strength of the geomagnetic field, meaning that a magnetometer cannot function efficiently because its accuracy is so reduced. Hence, the constant hard magnetic interference inside the vehicle is mainly concerned in this paper. An INS/Magnetometer heading estimation algorithm based on a two-stage Kalman filter is proposed to solve the problem by combining inertial sensor and magnetometer with attitude information. In the first stage filter, the constant hard iron interference is estimated by setting upward standing the three IMU axes. In the second stage filter, the INS/Magnetometer heading estimation is implemented. Finally, the results show that the algorithm improves the accuracy of vehicle heading calculations.

Funder

Natural Science Foundation of Heilongjiang Province of China

china scholarship council

national natural science foundation of china

Publisher

SAGE Publications

Subject

Applied Mathematics,Control and Optimization,Instrumentation

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