Searched for: subject%3A%22Structured%255C+kernel%255C+interpolation%22
(1 - 3 of 3)
document
Ban, Hanyuan (author)
Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor...
master thesis 2023
document
Fetter, Marnix (author)
Indoor positioning systems cannot rely on conventional localization methods, such as GPS, to locate devices because of interference with the structure of buildings. One solution is to use magnetic positioning, which is based on spatial variations in the patterns of the ambient magnetic field. To model magnetic fields, Gaussian process regression...
master thesis 2023
document
Menzen, C.M. (author), Fetter, Marnix (author), Kok, M. (author)
We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for 10-calization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides...
conference paper 2023