A Study on the Treatment of Temperature Drift in Fluxgate Magnetometers

Aiming at the problems of inaccurate measurement results and poor robustness of fluxgate magnetometers due to temperature drift during magnetic field measurement, this paper proposes an adaptive Kalman filtering algorithm combining least squares curve fitting and Kalman filtering optimal estimation algorithms as an estimator of magnetic field drift of fluxgate magnetometers, which is then combined with the temperature of fluxgate devices for evaluation. The system process noise and measurement noise are dynamically adjusted by the improved adaptive Kalman filter to minimize the effect of magnetic field temperature drift of the fluxgate magnetometer. The effectiveness of the estimator is verified by measuring experimental data with a constant magnetic field. After four rounds of iterative experiments, it is possible to achieve a magnetic field temperature drift of no more than 0.0024GS, enabling accurate measurement of the magnetic field using a flux gate magnetometer.