Minet Magnetic Localization

Michael Robert Drake, University of Mississippi

Abstract

Indoor localization is a modern problem of computer science that has no unified solution, as there are significant trade-offs involved with every technique. Magnetic localization is a less popular sub-field that is rooted in infrastructure-free design, which can allow universal setup. Magnetic localization is also often paired with probabilistic programming, which provides a powerful method of estimation, given a limited understanding of the environment. This thesis presents Minet, which is a particle filter based localization system using the Earth's geomagnetic field. It explores the novel idea of state space limitation as a method of optimizing a particle filter, by limiting the scope of possibilities the filter has to predict. Minet also is also built as a distributed model, which can be modified to integrate new technologies. Finally, the potential of improving Minet's base components is discussed, along with how different technologies such as a Deep Learning model can be implemented to improve performance.