A Novel Linear Calibration Model for Three-Axis Fluxgate Magnetometer

With low cost, simple structure and high accuracy, the three-axis fluxgate magnetometer (TFM) has been frequently used in many fields. However, the systematic errors are a major obstacle to the performance of TFM. Based on Taylor expansion and Cholesky decomposition, a new linear error calibration model is proposed from the general error model of TFM. In our new model, the parameter matrix can be decomposed to two parts: one is completely equivalent to the parameter matrix of the traditional linear model, and the other is a matrix containing second-order terms composed of error parameters, which was neglected in the traditional linear model. Thus, compared with the traditional model, our new linear model can acquire better calibration performances. In simulations, the functional link artificial neural network algorithm is provided to solve the model parameters. Simulation results show that the new model improves the calibration effect by 32.9 to 33.8% compared with the existing linear calibration model. After that, a single three-axis fluxgate magnetometer experimental platform is set up and related physical experiment is carried out. The results further prove the advantages and the practical value of the proposed model. Compared with the reference method, the effect is improved by 79.3%. The conclusions of this paper can expand the linear calibration method of three-axis magnetometer and improve the accuracy of such methods.