Deep-Learning-Enhanced Visible Light Positioning System Based on the LED Array

The escalating demand for indoor location-based services has propelled advancements in indoor positioning technologies in which visible light positioning (VLP) standing out for its accuracy and eco-friendliness. Current VLP systems are typically categorized into multianchor-based and single-anchor-based methods. The former encounters challenges like high wiring costs, limited adaptability in narrow spaces, and the segregation of lighting and positioning. Current single-anchor methods using cameras or photodiode (PD) arrays face complex hardware design costs and application limitations. To address these issues, a deep-learning-enhanced VLP method with a light-emitting diode (LED) array is proposed. First, a cost-effective single-anchor VLP system is designed, utilizing an LED array and a single PD, which dramatically reduces the costs. Second, to mitigate the impact of various noises on the received signal, a transformer-based signal-denoising network is designed, resulting in a significant reduction in the large ranging errors. Furthermore, a distance error estimation network based on graph neural network (GNN) is introduced to address the issue of poor anchor configuration caused by LED arrays. The GNN aggregates inherent correlations among LEDs, significantly reducing ranging errors. Finally, a weighted particle swarm optimization (PSO)-based positioning algorithm is introduced to further optimize results, and GNN's outputs are converted into weights for PSO using a self-designed weight function, enhancing accuracy and robustness. Experimental evaluations of the hardware and proposed methods demonstrate submeter-level positioning accuracy. Moreover, the effective coverage range is comparable to that of a regular LED fixture of the same size, suggesting its broad applications prospects in the Internet of Things (IoT) after it integrates lighting and positioning capabilities.