Passive Indoor Visible Light Positioning System Using Deep Learning

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.