Evaluation of Machine Learning Models for Received Signal Strength Based Visible Light Positioning with Obstacles

Abstract

Received Signal Strength based Visible Light Positioning (RSS-VLP) is an attractive solution for indoor positioning as an enabling technology for future smart environments. Despite this significant importance, there is currently no research focus on the influence of obstacles in the environment. Obstacles could prevent the mobile unit from reaching certain locations, or altogether block the line of sight between the receiver and the light sources. Therefore, this work compares the performance of different machine learning models for visible light positioning using a simulated environment where obstacles can be added. The results shows that machine learning models require much more training data to achieve acceptable performance when shadowing occurs. Gaussian Processes show the best performance out of all evaluated models in environments both with and without obstacles. However, the optimal attainable performance degrades when shadowing is introduced.