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.
Highlights
The Global Positioning System (GPS) has been widely used to provide location information in outdoor environments, but it fails to provide reliable positioning indoors [1]
Received signal strength (RSS) based Wi-Fi fingerprinting is a typical method frequently used for location estimation, since it does not need any prior knowledge of access points (APs) deployment
We have demonstrated in this article that the correlation between the RSS measurements when fusing fingerprints from multiple reference points (RPs) within a cell is too high to be ignored
Summary
The Global Positioning System (GPS) has been widely used to provide location information in outdoor environments, but it fails to provide reliable positioning indoors [1]. Wi-Fi based localisation system has attracted considerable attention because of the prevalent deployment of Wireless Local. The vast majority of current indoor localisation systems are designed for sub-metre accuracy in position estimation, which is unnecessary for most indoor navigation applications [3]. Received signal strength (RSS) based Wi-Fi fingerprinting is a typical method frequently used for location estimation, since it does not need any prior knowledge of access points (APs) deployment. The idea of the fingerprint technology is to use online RSS measurements to match the fingerprint database previously generated at every location in the offline training phase.
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