Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by Kalman Filtering

Magnetometer and inertial sensors are widely used for orientation estimation. Magnetometer usage is often troublesome, as it is prone to be interfered by onboard or ambient magnetic disturbance. The onboard soft-iron material distorts not only the magnetic field, but also the magnetometer sensor fra...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2018-03, Vol.26 (2), p.716-723
Main Authors: Wu, Yuanxin, Zou, Danping, Liu, Peilin, Yu, Wenxian
Format: Article
Language:English
Subjects:
Acceleration
Accelerometers
Calibration
Cross-sensor misalignment
gyroscope
Gyroscopes
Magnetic sensors
magnetometer calibration
Magnetometers
observability
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Summary:Magnetometer and inertial sensors are widely used for orientation estimation. Magnetometer usage is often troublesome, as it is prone to be interfered by onboard or ambient magnetic disturbance. The onboard soft-iron material distorts not only the magnetic field, but also the magnetometer sensor frame coordinate and the cross-sensor misalignment relative to inertial sensors. It is desirable to conveniently put magnetic and inertial sensors information in a common frame. Existing methods either split the problem into successive intrinsic and cross-sensor calibrations, or rely on stationary accelerometer measurements which are infeasible in dynamic conditions. This brief formulates the magnetometer calibration and alignment to inertial sensors as a state estimation problem, and collectively solves the magnetometer intrinsic and cross-sensor calibrations, as well as the gyroscope bias estimation. Sufficient conditions are derived for the problem to be globally observable, even when no accelerometer information is used at all. An extended Kalman filter is designed to implement the state estimation and comprehensive test data results show the superior performance of the proposed approach. It is immune to acceleration disturbance and applicable potentially in any dynamic conditions.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2017.2670527