Title :
Reducing magneto-inductive positioning errors in a metal-rich indoor environment
Author :
Orfeas Kypris;Traian E. Abrudan;Andrew Markham
Author_Institution :
Department of Computer Science, University of Oxford, Oxford, U.K. OX13QD
Abstract :
Ferrous objects distort magnetic fields and can significantly increase magneto-inductive positioning errors in indoor environments. In this work, we use image theory in order to formulate an analytical channel model for the magnetic field of a quasi-static magnetic dipole positioned above a perfectly conducting half-space. The proposed model can be used to compensate for the distorting effects that metallic reinforcement bars (rebars) impose on the magnetic field of a magneto-inductive transmitter node in an indoor environment. Good agreement is observed between the analytical solution and numerical solutions obtained from 2-D finite element simulations when the transmitter node is located more than 0.4 m above the distorters. Experimental results indicate that the image theory model shows significant improvement over the free space dipole model in estimating position along the normal to the plane of the rebars, typically reducing positioning errors by 36% in 90% of the cases.
Keywords :
"Magnetic resonance imaging","Magnetic moments","Numerical models","Channel models","Transmitters","Analytical models","Steel"
Conference_Titel :
SENSORS, 2015 IEEE
DOI :
10.1109/ICSENS.2015.7370199