A Crowd-Sensing Procrustes-Based Method for Indoor Positioning in a Common Frame of Reference

摘要

Indoor positioning techniques have been employed in a wide number of applications and have become attractive tools for emerging technologies like Internet of Things (IoT). Numerous indoor localization methods are highly constrained since they take into account the knowledge of building information, inter-node distances, some absolute position information and/or WiFi fingerprints. Here, we propose a less constrained crowd-sensing positioning method based only on dead reckoning information, and on relative positions of detected access points (APs). Our method comprises selection of APs, choice of a common frame of reference, a partial Procrustes transformation, and rotational and translational transformations applied to the trajectories of the users. By performing Monte Carlo simulations, the results show that the estimation of trajectories and APs performed by our crowd-sensing method is statistically accurate.