Title: Sensor network and inertial positioning hybridisation for indoor location and tracking applications

Authors: Yuri Álvarez López; Guillermo Álvarez Narciandi; Fernando Las-Heras Andrés

Addresses: Area of Signal Theory and Communications (TSC-UNIOVI), Department of Electrical Engineering, University of Oviedo, Edificio Polivalente, Módulo 8, Campus Universitario de Gijón, 33203 Gijón (Asturias), Spain ' Area of Signal Theory and Communications (TSC-UNIOVI), Department of Electrical Engineering, University of Oviedo, Edificio Polivalente, Módulo 8, Campus Universitario de Gijón, 33203 Gijón (Asturias), Spain ' Area of Signal Theory and Communications (TSC-UNIOVI), Department of Electrical Engineering, University of Oviedo, Edificio Polivalente, Módulo 8, Campus Universitario de Gijón, 33203 Gijón (Asturias), Spain

Abstract: An indoor location system (ILS) for practical asset and people tracking in indoor scenarios using received signal strength (RSS) ZigBee-based sensor network and inertial sensors is presented. A novel algorithm that uses differential signal levels gathered from a set of transmitter nodes is developed for processing RSS data. These levels are introduced into a cost function whose minimum gives the asset location estimation. The use of differential field levels-based algorithm avoids the need of system calibration due to signal strength fluctuation. Moreover, position accuracy is improved by adding inertial sensor information. The method is tested in a real scenario, demonstrating practical indoor positioning when combining ZigBee-based sensor network and inertial sensors information. The influence of the number of ZigBee nodes on the position estimation accuracy has been analysed.

Keywords: WSN; wireless sensor networks; ILS; indoor location systems; RSS; received signal strength; zigBee; inertial sensors; radio determination.

DOI: 10.1504/IJSNET.2016.10001270

International Journal of Sensor Networks, 2017 Vol.24 No.4, pp.242 - 252

Received: 17 Oct 2015
Accepted: 21 Feb 2016

Published online: 21 Aug 2017 *

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