光通信技术2024，Vol.48Issue(2)：42-47,6.DOI:10.13921/j.cnki.issn1002-5561.2024.02.008
基于卷积神经网络视觉成像可见光室内位置感知模型与光源布局
Visible light indoor positioning perception model and light source layout based on convolutional neural network visual imaging
摘要
Abstract
Aiming at the the problem of complex deployment,poor robustness,and low positioning accuracy in visible light in-door positioning systems based on received signal strength(RSS),a visible light indoor positioning perception model based on convolutional neural network(CNN)visual imaging is proposed,and the layout method of light sources is studied.Firstly,a visi-ble light visual imaging positioning perception model is designed and built based on ambient light and ordinary light-emitting diode(LED)light sources.Then,the deep features of the image are extracted through the pre-trained CNN model.Based on this,the positioning accuracy model of the indoor positioning perception model based on CNN visual imaging is optimized by study-ing the relationship between positioning accuracy and the number of light sources,as well as the spacing between light sources in different light source layout models.The experimental results show that compared with the indoor positioning perception model based on RSS,when the positioning errors are less than 2.1 cm and 3.9 cm respectively,the confidence probabilities of the pro-posed model are increased by 10%and 6.7%respectively.At the same time,compared with the rectangular layout method and the triangular layout method,the positioning accuracy of the cross layout method is improved by 9.5%and 16%respectively.关键词
可见光室内位置感知/视觉成像/光源布局/神经网络Key words
visible light indoor positioning perception/visual imaging/light source layout/neural network分类
信息技术与安全科学引用本文复制引用
李帅,张峰,孟祥艳,刘叶楠,张欣..基于卷积神经网络视觉成像可见光室内位置感知模型与光源布局[J].光通信技术,2024,48(2):42-47,6.基金项目
陕西省科技厅一般项目-工业领域(No.2022GY-072)资助 （No.2022GY-072）
西安工业大学优秀学位本文培育基金(YS202210)资助. （YS202210）
