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Automatic and Adaptive Correction of Diversionary Errors in Tri-Axial Magnetometer Using Neural Networks

A scalar calibration method is presented using artificial neural network to correct the diversionary errors in a tri-axial orthogonal magnetometer. Firstly, The relations are analyzed between the diversionary errors and magnetometer's "intrinsic" parameters, such as orthogonality between axes, amplification and bias of each axes. Then the calibration model with nine unknown parameters is established and a special neural network structure is devised, which can adaptively update the nine calibration parameters through the relationship between the outputs of the magnetometer and the magnetic field applied. A calibration experiment is described briefly, and the experimental results show that the proposed calibration method seems to be a very good candidate for fast or field calibration of a tri-axial orthogonal magnetometer.