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

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

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...

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Bibliographic Details
Main Authors: Yan Li, Simon Williams, Bill Moran, Allison Kealy, Guenther Retscher
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sensors
Subjects:
Multivariate Gaussian Mixture Model (MVGMM)
multivariate linear regression
Expectation-Maximisation imputation
Wi-Fi localisation
Hidden Markov Model (HMM)
Online Access:http://www.mdpi.com/1424-8220/18/8/2602

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http://www.mdpi.com/1424-8220/18/8/2602

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