Error Correction Method for Three-Axis Fluxgate Sensors Based on Genetic Algorithm

Abstract

Three-axis fluxgate sensors are widely applied in both military and civil fields. However, their measurement accuracy is affected by factors such as triaxial non-orthogonality, bias, and inconsistent scale factors, leading to the phenomenon of heading error and thus reducing the reliability of magnetic measurement data. To improve the measurement accuracy of the sensors, precise correction of systematic errors is required. This paper proposes an error correction method based on genetic algorithm (GA). First, the error sources of three-axis fluxgate sensors are theoretically analyzed, and corresponding mathematical models are established. On this basis, a nonlinear objective function containing error parameters is constructed, and the global optimization capability of GA is utilized to solve the optimal error parameters, ultimately achieving high-precision compensation for sensor errors. The simulation results show that this method can effectively correct the systematic errors of magnetic sensors and enhance their measurement accuracy.