Using independent component analysis for magnetic anomaly detection and localization of closely located targets
Eric Nieves, Pierre-Philippe J. Beaujean, Manhar Dhanak · AIP Advances · 2025
In this paper, the authors introduce a novel Magnetic Anomaly Differentiation and Localization Algorithm, which simultaneously localizes multiple magnetic anomalies with weak total field signatures (of the order of tens of nT) within Earth’s magnetic field. In particular, it focuses on the case where there are two homogeneous targets with known magnetic moments. This was done by analyzing the magnetic signals and adapting Independent Component Analysis (ICA) and simulated annealing (SA) to solve the problem statement. The results show the groundwork for using a combination of fastICA and SA to give localization errors to within 3 m or less per target in simulation and achieved a success rate of 58% for noiseless signals vs 44% success rate for noisy signals. Experimental results experienced additional errors due to the effects of magnetic background, unknown magnetic moments, and navigation error. While one target was localized to within 3 m, only the latest experimental run showed the second target approaching the localization specification. This highlighted the need for higher signal-to-noise ratio and equipment with better navigational accuracy. The data analysis was used to provide recommendations on the needed equipment to minimize observed errors and improve algorithm success.