Simultaneous Localization and Calibration (SLAC) Methods for a Train-Mounted Magnetometer

Abstract

Magnetic field localization is based on the fact that the Earth’s magnetic field is distorted in the vicinity of ferromagnetic objects. When ferromagnetic objects are in fixed positions, the distortions are also fixed and, thus, contain location information. In our prior work, we proposed a simultaneous localization and calibration (SLAC) algorithm based on a Rao-Blackwellized particle filter that enables magnetic train localization using only uncalibrated magnetometer measurements. In this paper, a lower-complexity version of the SLAC algorithm is proposed that only estimates a subset of calibration parameters. An evaluation compares the full and reduced SLAC approach to a particle filter in which the magnetometer is pre-calibrated with a fixed set of parameters. The results show a clear advantage for both SLAC approaches and that the SLAC algorithm with a reduced set of calibration parameters achieves the same performance as the one with a full set of parameters.