On Calibration of Three-axis Magnetometer

TL;DR

This paper proposes a quadratic optimal maximum likelihood calibration method for three-axis magnetometers, improving accuracy and stability over traditional approximate methods, especially in low excitation scenarios.

Contribution

It introduces a quadratic optimal ML estimation approach for magnetometer calibration, addressing shortcomings of existing approximate methods.

Findings

Optimal ML calibration outperforms approximate methods in accuracy.

The proposed method offers greater stability, especially with limited attitude excitation.

Benefits outweigh the increased computational complexity.

Abstract

Magnetometer has received wide applications in attitude determination and scientific measurements. Calibration is an important step for any practical magnetometer use. The most popular three-axis magnetometer calibration methods are attitude-independent and have been founded on an approximate maximum likelihood (ML) estimation with a quartic subjective function, derived from the fact that the magnitude of the calibrated measurements should be constant in a homogeneous magnetic field. This paper highlights the shortcomings of those popular methods and proposes to use the quadratic optimal ML estimation instead for magnetometer calibration. Simulation and test results show that the optimal ML calibration is superior to the approximate ML methods for magnetometer calibration in both accuracy and stability, especially for those situations without sufficient attitude excitation. The significant benefits deserve the moderately increased computation burden. The main conclusion obtained in the context of magnetometer in this paper is potentially applicable to various kinds of three-axis sensors.