Open AccessPrecision indoor three‐dimensional visible light positioning using receiver diversity and multi‐layer perceptron neural network
Author(s) -
Mahmoud Abdulrahman A.,
Ahmad Zahir Uddin,
Haas Olivier C.L.,
Rajbhandari Sujan
Publication year - 2020
Publication title -
iet optoelectronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 42
eISSN - 1751-8776
pISSN - 1751-8768
DOI - 10.1049/iet-opt.2020.0046
Subject(s) - indoor positioning system , multipath propagation , computer science , artificial neural network , multilayer perceptron , backpropagation , interference (communication) , positioning system , real time computing , artificial intelligence , telecommunications , acoustics , channel (broadcasting) , physics , accelerometer , node (physics) , operating system
In recent times, several applications requiring highly accurate indoor positioning systems have been developed. Since the global positioning system is unavailable/less accurate in the indoor environment, alternative techniques such as visible light positioning (VLP) are considered. The VLP system benefits from the wide availability of illumination infrastructure, energy efficiency and the absence of electromagnetic interference. However, there is a limited number of studies on three‐dimensional (3D) VLP and the effect of multipath propagation on the accuracy of the 3D VLP. This study proposes a supervised artificial neural network to provide accurate 3D VLP whilst considering multipath propagation using receiver diversity. The results show that the proposed system can accurately estimate the 3D position with an average root mean square (RMS) error of 0.0198 and 0.021 m for line‐of‐sight (LOS) and non‐LOS link, respectively. For 2D localisation, the average RMS errors are 0.0103 and 0.0133 m, respectively.