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Micromachines 2014, 5(4), 1012-1033; doi:10.3390/mi5041012

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

CIRGEO (Interdepartmental Research Center of Geomatics), University of Padova, via dell'Università 16, 35020 Legnaro (PD), Italy
These authors contributed equally to this work.
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Received: 4 September 2014 / Revised: 13 October 2014 / Accepted: 24 October 2014 / Published: 5 November 2014
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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Abstract

This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user’s point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user’s motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building. View Full-Text
Keywords: indoor navigation; positioning; sensor fusion; nonlinear filtering; smartphones geolocation indoor navigation; positioning; sensor fusion; nonlinear filtering; smartphones geolocation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Masiero, A.; Guarnieri, A.; Pirotti, F.; Vettore, A. A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation. Micromachines 2014, 5, 1012-1033.

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