Remote sensing of a magnetic target utilizing population based incremental learning

Magnetic anomaly detection (MAD) systems are used for decades to detect obscured ferromagnetic targets. We propose the population based incremental learning (PBIL) approach for localization of a static ferromagnetic target, and estimation of its magnetic moment. The ferromagnetic target is assumed as a magnetic dipole, which produces an anomaly in the ambient Earth magnetic field. The magnetic field is measured either by a mobile magnetometer or by a network of static magnetic sensors. The measured signal is processed utilizing PBIL algorithm, in order to characterize the magnetic target, i.e. localize the target and estimate its magnetic moment. The method has been tested by numerous computer simulations, and showed promising results. For SNR larger than 0.2 the localization error is less than 10% relative to closest proximity approach (CPA), and the magnetic moment estimation error is less than 20%. Applying the proposed method to a magnetic dipole target signal on the background of a real world magnetic noise proved to be consistent with simulation results.