Data-driven calibration of tri-axial magnetic sensors

Abstract This paper presents a data-driven calibration method for triaxial magnetic sensors that eliminates the need to solve sensor error model parameters. The calibration was achieved solely through the input and output of the sensor in a known magnetic field. Using both simulation and experimental data, this study employed a multilayer perceptron (MLP) algorithm as a data-driven calibration model. In static and dynamic calibration experiments conducted in a laboratory setting, the calibration performance of MLP was compared with that of traditional calibration algorithms. The experimental results demonstrate that in a static experiment under a 90 μT magnetic field, the sensor error after calibration using the data-driven method was reduced by 30.37% compared to the traditional method. In the dynamic experiments, the proposed method reduced the errors by 60.58% compared with the traditional method. The method proposed in this paper offers a new approach for improving the calibration accuracy and effective measurement range of triaxial magnetic sensors, and holds potential application value for other types of triaxial sensors.