RIS-Assisted Indoor Visible Light Positioning Based on Sparse Bayesian Learning

Visible light communication (VLC) relies heavily on the line of sight (LoS). Once the LoS link is blocked, the communication efficiency will be greatly reduced. The indoor environment is complex, and it is inevitable that the LoS link will be blocked. In order to solve such problem, we have integrated the reconfigurable intelligence surface (RIS) into the indoor visible light positioning (VLP) system to enhance the positioning accuracy when the LoS link is blocked. We have proposed a sparse Bayesian learning (SBL)-Based algorithm to solve the formulated joint-optimization problem of estimated position, noise variance, and sparsity. This algorithm uses Bayesian reconstruction theory and conditional probability expressions to calculate the mean and covariance matrix of the formula, and further obtain the updated noise variance value and sparsity value, thereby finally attain the accurate position. From the simulation results, the positioning accuracy can reach 0.41m when the cascade channel length is 10m.