Magnetostatics play a crucial role in the detection and localisation of naval vessels. Also, minimising a vessel’s magnetic signature is essential to reduce the risk posed by naval mines, which often rely on magnetic detection. This research aims to improve the calculation of magnetic signatures using the Method of Moments (MoM) by implementing it in Julia, a high-performance programming language. A simplified version of TNO’s current MATLAB-based approach is implemented in Julia to establish a baseline for the accuracy and efficiency. Linear basis functions and automatic differentiation (AD) are incorporated into the methodology to explore potential improvements. These extended methods are compared to the baseline to evaluate their performance.
Results show that Julia can be of great value, since it significantly improves the assembly time of the interaction matrix. Point matching is not a suitable approach when using linear basis functions. The Galerkin method shows promising results, though its computational performance remains a significant drawback. Also, using AD shows potential to simplify the implementation of the MoM by eliminating the need for analytical integral expressions. However, AD disappoints in terms of computational performance. Moreover, the AD implementation relies on a mesh-dependent parameter.

In this thesis a model for interpolation of magnetic fields is constructed using Gaussian processes. This model takes the curl- and divergence-free properties of magnetic fields into account. The Gaussian process regression, or kriging, is tested on both simulated and real data. It is also attempted to reconstruct the magnetization of objects based on measurement data.

This thesis explores different methodes to optimally guide a swarm of drones towards a steel shipping container. It does this using techniques such as Simulated Annealing and Magnetic Anomaly Detection. The drones will use Simulated Annealing in combination with Magnetic Anomaly Detection to estimate where the steel shipping container is located. To optimally share information between the different drones in the swarm four methods are proposed and analysed.