Magnetic-Field Feature Reduction for Indoor Location Estimation Applying Multivariate Models

Abstract

In the context of a magnetic field-based indoor location system, this paper proposes a feature extraction process that uses magnetic-field temporal and spectral features in order to develop a classification model of indoor places, using only a magnetometer included in popular smartphones. We initially propose 46 features, 26 derived from the spectral evolution and 20 from the temporal one, chosen because of the statistical potential to summarize the behavior of the signal. Nevertheless, in order to simplify the classification model, a genetic algorithm approach, combined with forward selection and back elimination strategies was applied. Our results show that is possible to reduce the magnetic-field signal features from 46 to only 6 features, and estimating the user's location with even better precision.