Visible Light Positioning System Based on SRU Neural Network in Coal Mine Underground

This study proposes a visible light positioning system to enhance the accuracy of underground positioning in coal mines and simplify the positioning system based on a simple circulation unit (SRU). The system comprises a single LED light and four photodetectors, where the four photodetectors are positioned on the front, back, left, and right positions of a safety helmet, with the point to be measured located at the top center of the helmet. The SRU neural network predicts the position information of the measured point. Simulation results show that within the positioning area of 3.6 m x 3.6 m x 3 m, the proposed system achieves a positioning accuracy of 1.42 cm, an average positioning time of 0.59 s, and 97% point positioning errors within 2.3 cm. Compared with other positioning algorithms, the proposed system demonstrates substantially enhanced positioning accuracy. To further validate the system's performance, the entire positioning system is implemented in an actual environment. The experimental results reveal an average positioning error of 10.21 cm, which meets the requirements for underground positioning in coal mines.