Magnetic field modeling

Abstract

Ferromagnetic materials in the walls and ground of buildings cause perturbations in the earth’s magnetic field. These spatial variations in the ambient magnetic field observed in indoor environments are mostly time-invariant. The deviations of the magnetic field can be captured in a magnetic field map, providing valuable information for indoor localization and navigation. Gaussian processes are a useful tool to model the ambient magnetic field, where the characteristics of the magnetic field are specified by the hyperparameters of the Gaussian process. To avoid the computational difficulties associated with full Gaussian process regression, a reduced-rank approximation is implemented. The magnetic field is measured using a magnetometer. A motion capture suit contains multiple magnetometers, whose relative positions are accurately determined. In this thesis it is researched if the quality of the magnetic field map can be improved when the measurement come from the motion capture suit. This is a challenging task, since the magnetometers are placed at different altitudes and the characteristics of the indoor magnetic field also vary with altitude. Consequently, the hyperparameters that best fit the measurement data are different per magnetometer. The negative effects of this observation are reduced by considering subsets of magnetometers operating at similar altitudes. However, an evident relation between the magnetometer’s altitude and its optimal hyperparameters has not been found. The hypothesis that complementing measurements from a single magnetometer with measurements of magnetometers operating at similar altitudes would improve the quality of the magnetic field is not supported based on the experiments. It is expected that either the magnetometer measurements lacked consistency, or that the assumption that the MOCAP suit returned the true magnetometer locations does not apply.