Localization of an Unknown Number of Magnetic Targets Based on 3-D Inversion Neural Network and Local Optimization Algorithm

Multiple magnetic targets localisation (MMTL) based on magnetic vector sensors has been widely used in UXO detection, medical application and marine targets monitoring. However, locating an unknown number of non-cooperative targets and targets with highly overlapping horizontal positions under sparse measurements is challenging. This study proposes a multiple magnetic targets localisation method that combines a 3-D inversion neural network with a local optimisation algorithm. The method divides the inversion space into a fixed grid and employs a 3D U-net network with a super-resolution module to reconstruct the 3D magnetic moment distribution in space. Then, the reconstructed distribution is processed using clustering method for preliminary positioning. Finally, the positions and magnetic moments of multiple targets are refined by the trust region reflective (TRR) algorithm. Compared with other methods, this method reduces the average positioning error of multiple targets, the magnetic moment estimation error, and the running time. Terrestrial experiments further demonstrate its effectiveness for unknown number of multiple magnetic targets and targets with overlapping horizontal positions.