Analysis of Device-Free and Device Dependent Signal Filtering Approaches for Indoor Localization Based on Earth's Magnetic Field System

Abstract

In this study, Earth's magnetic field signals have been investigated to determine mobile user's location. In theory, Earth's magnetic field does not change during the day at a certain point. But, the various noise effects that are exposed during the measurement causes deviations in the measured signal. In this study; Kalman Filter, LOESS, Savitzky-Golay filters are adapted with two different approaches to purge Earth's magnetic field values from noise. K-Nearest Neighbour and Random Forest models have been trained with filtered signals and the locations of the mobile user are determined. Relevant systems have been tested by using RFKONDB which is existed in literature. The purpose of this study is to measure how these filters should be adapted to an Earth's magnetic field based indoor localization systems. Digital sensors, which are integrated mobile devices, can use different measurement techniques. In a heterogeneous environment, noise reduction filters can show a different effect. Two different test scenarios and two different noise reduction models, with the 3 noise reduction techniques, are developed to find the best case.