A Visible Light Positioning System Based on a Particle Filter and Deep Learning

Currently, various indoor positioning technologies are widely studied, and visible light positioning (VLP) is a promising technology due to its high accuracy, low cost, and high output rate. However, the most common method based on the received signal strength (RSS) requires calibrating the model in advance, which has a weak generalization ability. This article focuses on the VLP method based on the time difference of arrival (TDOA), which does not require heavy preparatory work. First, we analyze the influence of different errors on TDOA-based VLP, such as the time synchronization error and receiver noise. Second, a convolution neural network (CNN)–based network is designed for phase difference estimation, which significantly improves the accuracy of phase difference estimation compared to the traditional in-phase and quadrature signal-based method. Finally, a particle filter based on the motion state is proposed to improve positioning accuracy and robustness. Simulated experiments evaluate the proposed methods, and the final results show a significant improvement in accuracy when compared with traditional methods. The improvements in ranging and localization accuracy can both reach over 50%.