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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest,  INSPEC (IET), EBSCO, CNKI.
    • E-mail ijiee@ejournal.net

University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2013 Vol.3(1): 127-131 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2013.V3.281

Hybrid Positioning through Extended Kalman Filter with Inertial Data Fusion

Rafiullah Khan, Francesco Sottile, and Maurizio A. Spirito

Abstract—In wireless sensor networks (WSNs), hybrid algorithms are widely used in order to improve the final positioning accuracy. This paper presents a hybrid positioning algorithm which combines time of arrival (TOA) and received signal strength (RSS) measurements using two different radio technologies, ultra wide band (UWB) and ZigBee, respectively. The TOA measurements are used to estimate the distances between a mobile node and a set of anchor nodes. Both UWB based distance estimates and RSS measurements based on ZigBee are simultaneously processed by an Extended Kalman Filter (EKF). Moreover, a low cost inertial device is also used to acquire acceleration measurements which proved to be useful in order to detect the motion of the mobile node. This information has also been integrated in the EKF algorithm accordingly. The performance of the final hybrid positioning algorithm is compared with the conventional EKF which uses a single type of range measurements, TOA or RSS. Simulation results based on a real measurements campaign, show that the hybrid algorithm significantly improves positioning accuracy. In addition, a further improvement has been achieved by applying the motion detection approach based on inertial measurements performed by the low cost acceleration sensor.

Index Terms—Hybrid Positioning, Extended Kalman Filter, TOA/RSS, Data fusion, Power measurement, Wireless Sensor Networks.

Rafiullah Khan is with Politecnico Di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy (email: rafiullah.khan@studenti.polito.it).
Francesco Sottileb and Maurizio A. Spirito are with Istituto Superiore Mario Boella (ISMB), 10138 Turin, Italy (email: rafiullah.khan@studenti.polito.it, {sottile, spirito}@ismb.it).


Cite: Rafiullah Khan, Francesco Sottile, and Maurizio A. Spirito, "Hybrid Positioning through Extended Kalman Filter with Inertial Data Fusion," International Journal of Information and Electronics Engineering vol. 3, no. 1, pp. 127-131, 2013.

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