Gaussian Mixture Modeling for Wi-Fi fingerprinting based indoor positioning in the presence of censored data

Main Article Content

Trung Kien Vu Hung Lan Le

Abstract

In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

 

DOI: https://doi.org/10.31276/VJSTE.61(1).03-08

Article Details

How to Cite
VU, Trung Kien; LE, Hung Lan. Gaussian Mixture Modeling for Wi-Fi fingerprinting based indoor positioning in the presence of censored data. Vietnam Journal of Science, Technology and Engineering, [S.l.], v. 61, n. 1, p. 3-8, mar. 2019. ISSN 2525-2461. Available at: <http://vietnamscience.vn/index.php/VJSTE/article/view/184>. Date accessed: 26 mar. 2019.
Section
MATHEMATICS AND COMPUTER SCIENCE