Calibration of three-axis magnetometer based on adaptive genetic algorithm

In view that the precision of MEMS magnetometer can not meet the heading measurement requirement of the attitude measurement system, the error source of a three-axis magnetometer is modeled and analyzed, and a calibration method based on ellipsoid fitting and adaptive genetic algorithm is proposed. The adaptive genetic algorithm is employed to fit an ellipsoid using raw data obtained by the three-axis magnetometer, and the output data is corrected by using the ellipsoid estimated parameters to compensate offset, scale factors, hard iron and soft iron. The three-axis direction cosines of the sensor are fitted by least square method so that the calibration of the sensor can remove the non-orthogonality and mounting error. Finally, the proposed calibration method is applied to an attitude reference system to conduct the heading measuring experiments using raw data and calibrated data. Experiment results demonstrate that the heading deviation range is reduced to 0.7° from 4.5°, which shows that the heading accuracy is increased by 6.8 times.