Using Data Pre-Processing and Convolutional Neural Network (CNN) to Mitigate Light Deficient Regions in Visible Light Positioning (VLP) Systems

Abstract

New systems and technologies, such as Internet-of-Things (IOT) may require high reliability and high accuracy indoor positioning and tracking of persons and devices in indoor areas. Among different visible-light-positioning (VLP) schemes, received-signal-strength (RSS) scheme is relatively easy to implement. RSS VLP scheme can provide high accuracy positioning if the optical channels between the Txs and Rxs, as well as the received optical powers of different LEDs are accurately known. Unfortunately, these conditions are not easy to achieve in practice. Due to the limited field-of-view (FOV) of the LED lamps, light deficient regions will happen. This light deficient region could be large and significantly affect the positioning accuracy when performing 3-dimentional (3-D) VLP since at these light deficient regions, very weak or even no optical signal is received. In this work, we put forward and demonstrate the RSS VLP system utilizing data pre-processing and convolutional neural network (CNN) to mitigate light deficient regions in VLP system. Traditional ANN model and linear regression (LR) model are also compared with the CNN model, and the results illustrate that the proposed scheme outperforms the other schemes by not only improving the positioning accuracy, but also the error distribution uniformity.