High-Resolution Detection, Localization, and Classification of Multiple Magnetic Dipole Sources

In recent years, significant progress has been made in detecting, localizing, and classifying multiple magnetic dipole sources (DLCMMS) based on magnetic gradient tensors. Traditional DLCMMS methods face challenges due to limited resolution and high-dimensional parameter optimization. In response, we propose a novel DLCMMS method within the cooperative coevolution (CC) framework. Specifically, we introduce a decomposition strategy within the CC framework to partition the original DLCMMS problem into lower dimensional subproblems. We perform joint parameter optimization (JPO) for source number, positions, and magnetic moment parameters in each subproblem covering a subset of magnetic sources and measurement points. Moreover, this letter enhances the hybrid optimization algorithm, combined with differential evolution and Levenberg–Marquardt, for JPO. In simulations, the number recognition accuracy of sources is 99.3% without noise, about three times higher than traditional methods. During a field demonstration, the position estimation error was below 0.053 m.