光通信技术2025，Vol.49Issue(1)：25-30,6.DOI:10.13921/j.cnki.issn1002-5561.2025.01.005
生物启发多特征融合学习的室内可见光位置感知方法
Bio-inspired multi-feature fusion learning method for indoor visible light position sensing
摘要
Abstract
To enhance the robustness and positioning accuracy of the Elman indoor visible light position sensing model,a bio-in-spired multi-feature fusion learning method for indoor visible light position sensing is proposed.This method first preprocesses the acquired visible light images to ensure the accuracy of feature extraction.Then,by fusing features from different levels of a pre-trained neural network model,it constructs a position-sensing feature library,thereby enhancing feature representation capa bility and richness,which improves the model's position sensing precision.Finally,the dung beetle optimization(DBO)algo-rithm is employed to optimize the topology and weight parameters of the Elman neural network,addressing issues where tradi-tional Elman neural networks easily fall into local optima in indoor position sensing,accelerating convergence speed,and enhanc-ing generalization performance.The experimental results show that within a 3D space of 4 mx3.5 mx3 m,the proposed algorithm achieves an average positioning error of 0.21 m,with 91.3％probability of average positioning error is less than 0.4 m,improving positioning accuracy by 22.3％compared to the Elman algorithm.关键词
室内可见光位置感知/视觉成像/蜣螂优化算法/Elman神经网络Key words
indoor visible light position sensing/visual imaging/dung beetle optimization algorithm/Elman neural network分类
电子信息工程引用本文复制引用
韦吉月,张峰,孟祥艳,赵黎,李帅..生物启发多特征融合学习的室内可见光位置感知方法[J].光通信技术,2025,49(1):25-30,6.基金项目
国家自然科学基金项目(12004292)资助 （12004292）
陕西省科技厅一般项目-工业领域(12022GY-072)资助 （12022GY-072）
西安市科技局高校院所科技人员服务企业项目(124GXFW0034)资助. （124GXFW0034）
