Gaussian Process for Propagation Modeling and Proximity Reports Based Indoor Positioning

The commercial interest in proximity services is increasing. Application examples include location- based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile dev...

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Bibliographic Details
Published in2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) pp. 1 - 5
Main Authors Yuxin Zhao, Feng Yin, Gunnarsson, Fredrik, Amirijoo, Mehdi, Hendeby, Gustaf
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Adaptation models
Atmospheric measurements
Computational modeling
Gaussian processes
Ground penetrating radar
Mobile handsets
Particle measurements
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Summary:The commercial interest in proximity services is increasing. Application examples include location- based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.
DOI:10.1109/VTCSpring.2016.7504255