Machine Learning in Indoor Visible Light Positioning Systems: A Review

Abstract

Developing a wireless indoor positioning system with high accuracy, reliability, and reasonable cost has been the focus of many researchers. Recent studies have shown that visible-light-based positioning (VLP) systems have better positioning accuracy than radio-frequency-based systems. A notable highlight of those research articles is their combination of VLP and machine learning (ML) to improve the positioning performance in both two-dimensional and three-dimensional spaces. In this paper, in addition to describing VLP systems and well-known positioning algorithms, we analyze, evaluate, and summarize the ML techniques that have been applied recently. We break these into four categories: supervised learning, unsupervised learning, reinforcement, and deep learning. We also provide deep discussion of articles published during the past five years in terms of their proposed algorithm, space (2D/3D), experimental method (simulation/experiment), positioning accuracy, type of collected data, type of optical receiver, and number of transmitters.