Enhanced Dipole Model-Based Magnetic Disturbance Compensation Using Magnetometer Arrays

Magnetometers are widely used in robotics and localization systems but are susceptible to magnetic disturbances generated by nearby ferromagnetic objects, which degrade their accuracy. Traditional calibration methods often fail in dynamic environments, such as those encountered by mobile robots. This paper investigates a dipole model-based disturbance compensation method using a magnetometer array with increased sensor density, extending prior configurations with fewer sensors. The method leverages a detection system to locate disturbing objects, models them as magnetic dipoles, and estimates their parameters through optimization. Experimental validation was performed using magnetic fingerprints of metallic objects in multiple configurations. The results show that increasing sensor density significantly improves compensation performance, reducing magnetic field errors to below 6.64 μT and heading errors to 0.31 rad in most scenarios. In low-to-moderate disturbance scenarios, the four-sensor array achieved heading error improvements of approximately 13% compared to the uncompensated case. In contrast, the proposed nine-sensor array achieved improvements exceeding 50%. In highly complex scenarios involving multiple overlapping disturbances, performance degrades, highlighting limitations of the dipole-based model. These results indicate that increasing sensor density enhances robustness and suggest that adopting compact array geometries may further improve performance in highly disturbed scenarios.