Paper Info
Title
Falcon: Fused Application of Light Based Positioning Coupled With Onboard Network Localization
Abstract
Indoor localization based on visible light and visible light communication has become a viable alternative to radio frequency wireless-based techniques. Modern visible light position (VLP) systems have been able to attain sub-decimeter level accuracy within standard room environments. However, a major limitation is their reliance on line-of-sight visibility between the tracked object and the lighting infrastructure. This paper introduces fused application of light-based positioning coupled with onboard network localization (Falcon), a VLP system, which incorporates convolutional neural network-based wireless localization to remove this limitation. This system has been tested in real-life scenarios that cause traditional VLP systems to lose accuracy. In a hallway with luminaires along one axis, the Falcon managed to attain position estimates with a mean error of 0.09 m. In a large room where only a few luminaires were visible or the receiver was completely occluded, the mean error was 0.12 m. With the luminaires switched off, the Falcon managed to correctly classify the target 99.59% of the time to within a 0.9-m(2) cell and with 100% accuracy within al.6-m(2) cell in the room and hallway, respectively.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2847314
IEEE ACCESS
Keywords
Field
DocType
Indoor positioning systems (IPS),indoor localization,visible light communication (VLC),visible light positioning (VLP),zigbee localization,convolutional neural network (CNN)
Falcon,Visibility,Wireless,Computer science,Convolutional neural network,Computer network,Mean squared error,Visible light communication,Real-time computing,Radio frequency
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Daniel Konings100.34
Baden Parr200.34
Fakhrul Alam3209.06
Edmund Ming-Kit Lai412058.89