A Correction Method for Fluxgate Magnetometers Based on PSO‐SA

Addressing three‐component errors in fluxgate magnetometers, this study introduces a novel error correction approach based on particle swarm optimization with simulated annealing (PSO‐SA). Through an exhaustive analysis of error sources, a robust correction model is formulated. PSO‐SA seamlessly integrates a simulated annealing mechanism into the conventional PSO algorithm, enhancing its capability to evade local optima and achieve superior global search proficiency. The method’s viability is rigorously validated via comprehensive numerical simulations. An experimental framework is meticulously devised to rectify three‐component errors in fluxgate magnetometers using particle swarm optimization (PSO), particle swarm‐genetic algorithm (PSO‐GA), and the proposed PSO‐SA. Results distinctly reveal the remarkable superiority of PSO‐SA over PSO in terms of correction efficacy, yielding a substantial 38% enhancement in calibration precision and a notable 20% escalation in calibration speed compared to PSO‐GA. However, it is prudent to acknowledge that although the corrective outcomes closely approximate the values garnered from standard magnetometer measurements, they do not warrant exact representation of the authentic magnetic field. Consequently, the algorithmic methodology cannot entirely substitute the manual vector calibration technique. Nonetheless, this innovative approach presents a swift and precise corrective strategy devoid of the prerequisite for specialized laboratory setups. It showcases accelerated convergence and heightened accuracy, thus rendering it an apt contender for meticulous fluxgate magnetometer calibration in pragmatic real‐world scenarios.