Passive Indoor Visible Light Positioning System Using Deep Learning

Abstract

A passive indoor visible light positioning system is proposed that does not require active participation from the user and is suitable for IoT sensor networks. This approach does not require a line-of-sight path and measures the impulse response (IR) between sources and receivers installed in the room. The presence of an object of interest (OI), i.e., a person to be localized, disrupts the IRs among the source–receiver pairs, which can be related to its position. A deep learning framework is developed that learns the relationship between changes in sets of IRs and the OI position through a set of training data obtained by placing the OI at random locations in the room. This approach shows that the OI can be localized using a very limited set of training data under a wide range of illumination levels. In order to represent a realistic scenario, a room with furniture is modeled in the optical system design software. The ray trace information of the modeled room is used to construct IR measurements among different source–receiver pairs that include multiorder reflections. The results show that localization performance is crucially related to the signal-to-noise ratio and the number of training data points used in the learning process. A root-mean-square error (RMSE) near 30 cm is possible in the case of high SNR and a large training set. However, even with a very limited training set and over a range of dimming levels, RMSEs of near 80 cm were obtained without the need for explicit user involvement.