Examination of Indoor Localization Techniques and Their Model Parameters

Abstract

The future, in which mobile robots and human beings intermingle in industrial complexes, shopping malls, airports, and similar areas, is not far. The condition, however, requires the realization of several features, including self-localization, self-navigation, identification and avoidance of obstacles, and dynamic route discovery. Indoor localization has been the subject of interest for over two decades, most recent advances attempting to take advantage of freely available signals from a plethora of indoor sources. As far as the estimation task is concerned, most of the models employ extended kalman filters and particle filters. Each technique has its own merits and demerits, as well as a set of assumptions. The purpose of this paper to identify the most significant components of these techniques and to closely examine the prevailing assumptions underlying the selection process of indoor localization techniques. Moreover, the paper experimentally demonstrates the error modeling process in kalman and particle filters.