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.
Highlights
Vehicle navigation systems can provide real-time and accurate navigation information
During global positioning system (GPS) outages, INS systems based on Inertial Measurement Unit (IMU), can be used to maintaining a continuous vehicle navigation solution as an alternative.[2,3]
Two IMUs are mounted in parallel and placed horizontally in the vehicle
Summary
Vehicle navigation systems can provide real-time and accurate navigation information. A constant hard iron interference compensation method is proposed to improve the vehicle heading accuracy of an integrated INS/Magnetometer system. The method can be used to build a more accurate nonlinear model without the need for initial parameters estimation It improves the calibration accuracy, the algorithm increases the computational burden.[18,19] Many researchers focused on magnetometer calibration methods based on magnetometer/inertial sensor fusion.[20] since low-cost inertial sensors have complex error characteristics, achieving simultaneous calibration of the magnetometer and inertial sensors is a key to improving the magnetic heading information. The structure of the rest of this paper is as follows: Section 2 proposes a constant hard iron interference estimation method based on IMU three axes upward standing.
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