Magnetic moments estimation and localization of a magnetic object based on Hopfield neural network

Magnetic anomaly detection has been applied in civil and military areas. In the inverse problems of magnetic anomaly detection, a mathematical model was founded to estimate magnetic moments by minimizing the least-squares error at the observation points. Hopfield neural network was applied to solve the above optimization model and a self-adaptive correction algorithm was taken to improve the robustness of the model. Then, based on the Hopfield neural network energy function, a technique of magnetic source localization was provided in this paper. By the numerical simulations of magnetic moments estimation and localization of a magnetic object, it is shown that the technique has high accuracy, good robustness, easy implementation and is a practicable method in magnetic anomaly detection.