Artificial Neural Network Based Indoor Positioning in Visible Light Communication Systems

In this study, an artificial neural network (ANN) based visible light positioning system is proposed. For this purpose, an empty and closed room scenario is considered. In this scenario, a LED (light emitting diode) bulb is the transmitter and a photodiode is the receiver. The communication channel is modelled as a visible light channel which take into acoount of multipath reflections. In this regard, Combined Deterministic and Modified Monte Carlo (CDMMC) method is used. Then the proposed system and the RSS-based system in the literature are compared with each other in terms of positioning performances in indoor environment. The RSS-based indoor positioning system have high positioning errors due to the internal reflections into the room. As a result of this study, the ANN-based positioning system has much higher accuracy and near-realistic results than the RSS-based positioning system has. The disturbing effects of multipath reflections on localization has been considerably removed using ANN-based methods.