Indoor human tracking mechanism using integrated onboard smartphones Wi-Fi device and inertial sensors

Safar A. Maghdid (), Halgurd S. Maghdid (), Sherko R. HmaSalah, Kayhan Z. Ghafoor (), Ali Safaa Sadiq () and Suleman Khan ()
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Safar A. Maghdid: Koya University
Halgurd S. Maghdid: Koya University
Sherko R. HmaSalah: Koya University
Kayhan Z. Ghafoor: Salahaddin University-Erbil
Ali Safaa Sadiq: Monash University
Suleman Khan: Monash University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2019, vol. 71, issue 3, No 10, 447-458

Abstract: Abstract In indoor/outdoor environments, special cares need to be given to locate smartphones which are used by most of the people. Locating or tracking is valuable for those people who are in dangerous falling-situations or they are used for shopping and billing services, inside the buildings. This tracking system needs a new positioning mechanism to offer very accurate services to the special needy people. To this end, this paper presents a hybrid mechanism to locate indoors smartphones; specifically Wi-Fi access-points signals are available. The proposed mechanism incorporates onboard Wi-Fi and sensor devices including gyroscopes and accelerometers to provide accurate indoor smart phone positioning. This paper proposes an integrated approach to offer indoor smartphone positioning. The purpose of the integrated approach is to fuse multi-technologies measurements on smartphones. The mechanism uses proximity-level (based on received-signal-strength ‘RSS’ measurement) technique between the smartphone and Wi-Fi access-points which they are exist in the vicinity. Then it combines this proximity measurement with uncertainty calculations from onboard dead-reckoning measurements using Extended-Kalman filter, which can provide seamless, low cost, and improve location accuracy significantly, especially when deep indoor. This means, in deep indoor, the approach can utilize only a single Wi-Fi access-points signals as well as using prior-estimate positions based on artificial conditions. The results from different trial experiments (using Android-based smartphones) show that around 2.5-m positioning accuracy can be obtained.

Keywords: Smartphones; Wi-Fi/sensors; RSS; Kalman filter; Indoor positioning (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11235-018-0517-2

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