Abstract
In this work, we propose and demonstrate a received-signal-strength (RSS) based visible-light-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions.
Highlights
Due to the growing popularity of Internet-of-Thing (IoT), smart mobile devices and artificial intelligence (AI), the demand for highly precise indoor positioning is increasing rapidly
The position errors of the kernel ridge regression machine learning (KRRML) algorithm is quite low, the introduction of the proposed sigmoid function data preprocessing (SFDP) can still reduced by the positioning errors from 2.0 to 3.1 %
We proposed and demonstrated an RSS based visible light positioning (VLP) system using SFDP method; and applied it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the KRRML algorithm
Summary
Due to the growing popularity of Internet-of-Thing (IoT), smart mobile devices and artificial intelligence (AI), the demand for highly precise indoor positioning is increasing rapidly. Besides GPS, systems such as Bluetooth, Radio Frequency Identification (RFID), Wireless-Fidelity (Wi-Fi), UltraWide Band (UWB), etc. The UWB technology is based on transmitting radio-frequency (RF) short pulses with a low duty cycle. This allows accurate localization and tracking of mobile devices in indoor environments [2]. These RF based systems could suffer from interferences generated by other RF wireless devices and may not be applicable in RF restricted areas. Visible light communication (VLC) is considered as a promising wireless technology for the future wireless and mobile
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