煤矿井下基于SRU神经网络的可见光定位系统  

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

作　　者：汝贵 秦岭 王凤英[1] 胡晓莉[1] 徐艳红[1] Ru Gui;Qin Ling;Wang Fengying;Hu Xiaoli;Xu Yanhong(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,Inner Mongolia,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《激光与光电子学进展》2024年第14期237-244,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62161041);内蒙古自然科学基金项目(2022MS06012);内蒙古关键技术攻关项目(2021GG0104)。

摘　　要：为了提高煤矿井下定位的精度并降低定位系统的复杂度,提出一种基于简单循环单元(SRU)的煤矿井下可见光定位系统。该系统由单个LED灯和4个光电探测器(PD)组成,其中4个光电探测器分别位于安全帽的前后左右四个位置,待测点位于安全帽顶部的中心,通过SRU神经网络预测待测点的位置信息。仿真结果表明,在3.6 m×3.6 m×3 m的定位区域内,所提系统的定位精度可以达到1.42 cm,平均定位时间为0.59 s,且97%的点定位误差都在2.3 cm内,与其他定位算法相比,定位精度得到显著提升。为了进一步验证该定位系统的性能,在实际环境中搭建了整个定位系统,实验结果表明,所提定位系统的平均定位误差为10.21 cm,能够满足煤矿井下定位的要求。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×3.6 m×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.

关 键 词：简单循环单元 深度学习 可见光 煤矿井下 定位系统 

分 类 号：TN929.1[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]