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Sensors 2018, 18(8), 2602; https://doi.org/10.3390/s18082602

High-Dimensional Probabilistic Fingerprinting in Wireless Sensor Networks Based on a Multivariate Gaussian Mixture Model

1
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
2
Department of Geospatial Science, RMIT University, Melbourne, VIC 3000, Australia
3
Department of Geodesy and Geoinformation, TU Wien-Vienna University of Technology, Gusshausstrasse 27-29, E120/5, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Received: 12 July 2018 / Revised: 6 August 2018 / Accepted: 6 August 2018 / Published: 8 August 2018
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
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Abstract

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. Firstly, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Secondly, heterogeneous devices record different received signal strength (RSS) levels because of the variations in chip-set and antenna attenuation. Thirdly, APs are not necessarily visible in every scanning cycle leading to missing data issue. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. To account for spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP was investigated in this research, and demonstrated the efficiency as a beneficial information to differentiate between cells. The proposed system is able to achieve comparable localisation performance. Filed test results achieve a reliable 97% localisation room level accuracy of multiple mobile users in a real university campus Wi-Fi network. View Full-Text
Keywords: Multivariate Gaussian Mixture Model (MVGMM); multivariate linear regression; Expectation-Maximisation imputation; Wi-Fi localisation; Hidden Markov Model (HMM) Multivariate Gaussian Mixture Model (MVGMM); multivariate linear regression; Expectation-Maximisation imputation; Wi-Fi localisation; Hidden Markov Model (HMM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, Y.; Williams, S.; Moran, B.; Kealy, A.; Retscher, G. High-Dimensional Probabilistic Fingerprinting in Wireless Sensor Networks Based on a Multivariate Gaussian Mixture Model. Sensors 2018, 18, 2602.

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