  Visible light positioning(VLP) has gained prominence as a highly accurate
indoor positioning technique. Few techniques consider the practical limitations
of implementing VLP systems for indoor positioning. These limitations range
from having a single LED in the field of view(FoV) of the image sensor to not
having enough images for training deep learning techniques. Practical
implementation of indoor positioning techniques needs to leverage the ubiquity
of smartphones, which is the case with VLP using complementary metal oxide
semiconductor(CMOS) sensors. Images for VLP can be gathered only after the
lights in question have been installed making it a cumbersome process. These
limitations are addressed in the proposed technique, which uses simulated data
of a single LED to train machine learning models and test them on actual images
captured from a similar experimental setup. Such testing produced mean three
dimensional(3D) positioning error of 2.88 centimeters while training with real
images achieves accuracy of less than one centimeter compared to 6.26
centimeters of the closest competitor.
