Synchronous correction of two three-axis magnetometers using FLANN

An analytical model for the correction and compensation of a magnetic flux-gate gradiometer composed of two three-axis magnetometers (TAMs) using sensor rotation motion in a constant magnitude field is presented. Sensor motion is parameterized in terms of elementary rotations by many times about three sensitive axes. A measurement error model for fluxgate magnetic scalar gradiometer is established, and its error correction method based on functional link artificial neural network (FLANN) is proposed. The numerical simulations and experimental tests prove good convergence of the algorithm, which can depress remarkably the measurement error of magnetic magnitude gradient. Therefore, the research provides a feasible way to improve the performance of magnetic scalar gradiometer.

► We establish a measurement error model for two three-axis fluxgate magnetometers.
► We propose synchronous correction method of two three-axis fluxgate magnetometers based on functional link artificial neural network.
► We perform numerical simulations and experimental tests to prove good convergence of correction algorithm.