Abstract

This thesis presents a novel method to improve the accuracy of Pedestrian Dead Reckoning (PDR) localization in indoor environments. PDR, that is widely used for indoor positioning, tracks the location of a target by integrating measurements of step length and heading provided by inertial sensors. Step length can be estimated relatively accurately, whereas the estimation of heading in indoor environments is highly problematic. A common way to estimate the heading in PDR is to use magnetometer measurements; however, unlike outdoor environments, the Earth's magnetic field is strongly perturbed inside buildings making the magnetometer measurements unreliable for heading estimation. The main purpose of this work is to estimate the heading of a group of people that are walking in the same direction by using only magnetic sensors embedded into smartphones. The novelty of this work is in the proposed method: in a fi rst phase the PDR system fi lters perturbed estimates by means of a machine learning algorithm, in a second phase it exploits collaboration among users to fuse multiple heading estimates and gain spatial diversity. Fusion approach has been realized both in centralized and in distributed way; in particular, diff erent consensus algorithms are proposed to test performance in terms of convergence time and localization accuracy at convergence. Several measurement campaigns have been conducted by means of smartphones in order to analyze magnetic sensor properties in indoor environments and evaluate the PDR performance. The encouraging results show that by the proposed approach the heading estimate error is …

Metadata

publication

Politecnico di Milano, 2013

year

2013

publication date

2013

authors

LUCA LUCERI

link

https://www.politesi.polimi.it/handle/10589/102682

publisher

Politecnico di Milano