基于深度学习的可见光通信系统室内三维定位  

Deep learning based indoors three dimensional positioning for visible light communication system

作　　者：马玉磊[1] 张兵 MA Yulei;ZHANG Bing(Department of Computer and Information Engineering,Xinxiang University,Xinxiang 453000,China)

机构地区：[1]新乡学院计算机与信息工程学院,河南新乡453000

出　　处：《光学技术》2024年第2期201-208,共8页Optical Technique

基　　金：河南省科技厅重点研发与推广专项(科技攻关)项目(212102210405);2022年度新乡学院教育教学改革研究与实践项目成果(31)。

摘　　要：针对目前室内可见光通信系统三维定位的准确率与定位速度依然不佳的问题,提出一种基于深度学习的可见光通信系统室内定位方法。首先,设计了一个神经网络将指纹数据编码成二维阵列,利用卷积神经网络学习指纹阵列与目标位置之间的关系;然后,通过粒子群优化算法自动搜索卷积神经网络的超参数,以降低深度神经网络的训练难度。此外,设计了定位数据训练集、验证集与测试集的划分方法,有助于缓解神经网络的过拟合问题,并提高定位准确性。仿真结果表明,所提方法在6×6×4m3室内环境下的平均定位误差为0.024m,平均定位时间为0.478s。Aiming at the problem that both accuracy and speed of the present three dimensional positioning techniques in the visible light communication system are still not good,an indoors positioning method for visible light communication system based on deep learning is proposed.Firstly,a neural network is designed to encode the fingerprint data to two dimensional array,and the convolutional neural network is utilized to learn the relationship between the fingerprint array and the target position;Then,the hyperparameters of the convolutional neural network are tuned automatically by the particle swarm optimization algorithm,thereby the training difficulty of the deep neural network is reduced.Besides,a method of dividing the training set、verification set and test set for the visible light positioning dataset is designed,it helps to mitigate the overfitting problem of the neural network,and improve the positioning accuracy.Simulation results show that,the average positioning error of the proposed method is 0.024min 6×6×4m3 indoors environment,and the average positioning time is 0.478s.

关 键 词：可见光通信系统 室内定位 信号强度检测 前馈神经网络 卷积神经网络 指纹正则化 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]