High-dimensional probabilistic fingerprinting in wireless sensor networks based on a multivariate gaussian mixture model

Li, Y, Williams, S, Moran, B, Kealy, A and Retscher, G 2018, 'High-dimensional probabilistic fingerprinting in wireless sensor networks based on a multivariate gaussian mixture model', Sensors (Switzerland), vol. 18, no. 8, pp. 2602-2626.


Document type: Journal Article
Collection: Journal Articles

Title High-dimensional probabilistic fingerprinting in wireless sensor networks based on a multivariate gaussian mixture model
Author(s) Li, Y
Williams, S
Moran, B
Kealy, A
Retscher, G
Year 2018
Journal name Sensors (Switzerland)
Volume number 18
Issue number 8
Start page 2602
End page 2626
Total pages 25
Publisher Copernicus GmbH
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.
Subject Navigation and Position Fixing
Keyword(s) Expectation-Maximisation imputation
Hidden Markov Model (HMM)
Multivariate Gaussian Mixture Model (MVGMM)
Multivariate linear regression
Wi-Fi localisation
DOI - identifier 10.3390/s18082602
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution (CC BY) license.
ISSN 1424-8220
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