光通信技术2024，Vol.48Issue(2)：36-41,6.DOI:10.13921/j.cnki.issn1002-5561.2024.02.007
基于Bi-LSTM神经网络的室内可见光定位方法
Indoor visible light positioning method based on Bi-LSTM neural network
摘要
Abstract
Due to the large number of hyperparameters,it is difficult to obtain the optimal system model for the bidirectional long short-term memory(Bi-LSTM)neural network.At the same time,considering the possibility of premature convergence in the Grey Wolf optimizer(GWO)algorithm,a single-lamp localization method using the GWO combined with particle swarm opti-mization(GWO-PSO)algorithm to optimize the Bi-LSTM neural network is proposed.By optimizing hyperparameters such as the learning rate and the number of hidden neurons in the network,the stability and positioning accuracy of the system are im-proved.Finally,the weighted K-nearest neighbors(WKNN)algorithm is used to optimize points with large errors to obtain more accurate positioning locations.The simulation results show that in an indoor environment of 3 m×3.6 m×3 m,the average posi-tioning error of the proposed localization method is 3.57 cm,with 90%of the positioning errors within 6 cm.关键词
可见光定位/双向长短时记忆/灰狼结合粒子群/加权K近邻Key words
visible light positioning/bidirectional long short term memory/Grey Wolf combined with particle swarm optimiza-tion algorithm/weighted K-nearest neighbor分类
信息技术与安全科学引用本文复制引用
王乐乐,秦岭,胡晓莉,赵德胜..基于Bi-LSTM神经网络的室内可见光定位方法[J].光通信技术,2024,48(2):36-41,6.基金项目
国家自然科学基金项目(62161041)资助 （62161041）
内蒙古自治区应用技术研究与开发资金项目(2021GG0104)资助. （2021GG0104）
