Indoor visible light positioning system for Dual-LED based on Transformer-Encoder model

In visible light positioning (VLP) systems, factors like multipath reflection and noise interference degrade positioning accuracy. To address this challenge, this letter innovatively applies a Transformer-Encoder model to a VLP system with a dual-LED and multi-photodiode (PD) architecture. The proposed Transformer-Encoder model captures the spatial distribution information of the PDs through a Positional Encoding module. Its core Multi-Head Attention mechanism, incorporating positional information, enables the model to focus on critical channel features. This significantly enhances the model's robustness against multipath interference and noise while strengthening its capability to characterize channel features. Simulation results demonstrate that within a 4 m × 4 m × 3 m space, the Transformer-Encoder model achieves an average positioning error of 0.75 cm, with 90% of errors below 1.27 cm. Comparative analysis with other positioning models confirms the high precision of the proposed method.