K-means+SSA-Elman网络可见光室内位置感知算法  

K-means+SSA-Elman network visible light indoor location awareness algorithm

作　　者：李宝玉 张峰[1] 彭侠[2] 刘叶楠[1] LI Baoyu;ZHANG Feng;PENG Xia;LIU Yenan(School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710065,China;Xi'an Institute of Applied Optics,Xi'an 710065,China)

机构地区：[1]西安工业大学电子信息工程学院,陕西西安710065 [2]西安应用光学研究所,陕西西安710065

出　　处：《应用光学》2022年第3期453-459,共7页Journal of Applied Optics

基　　金：陕西省重点研发计划项目(2020GY-055)。

摘　　要：由于室内环境复杂,基于Elman神经网络的可见光位置感知存在收敛速度慢、定位精度低等缺点。论文提出基于麻雀搜索算法(sparrow search algorithm,SSA)优化Elman神经网络,同时融合K-means聚类的一种可见光室内位置感知算法。对采集到的数据建立数据库,利用SSA对Elman的拓扑结构和连接权阈值进行优化,建立训练模型,解决基于Elman神经网络室内位置感知算法易陷入局部最优的问题,提高收敛速度和稳健性;利用K-means对数据库优化分类,将处理好的数据代入模型训练得初步预测结果;将初步预测结果代入子类二次训练得预测点的最终坐标,进一步提高定位精度。基于0.8 m×0.8 m×0.8 m的立体空间进行实验,结果表明:论文算法平均定位误差3.22 cm,定位误差小于6 cm,概率达到90%,相较SSA-Elman算法定位精度提高7.5%;相较Elman网络算法定位精度提高16%。Due to the complex indoor environment,the visible light location awareness based on Elman neural network has the problems of slow convergence speed and low positioning accuracy.An optimized Elman neural network based on sparrow search algorithm(SSA)was proposed,and a visible light indoor location awareness algorithm was fused with K-means clustering.The database was established for the collected data,the topological structure and connection weight threshold of the Elman were optimized by using SSA,and the training model was designed,so as to solve the problem that the indoor location awareness algorithm based on Elman neural network was easy to fall into the local optimization and improve the convergence speed and robustness.The K-means was used to optimize the classification of database,and the processed data was substituted into the model training to obtain the preliminary prediction results.The preliminary prediction results were substituted into the subclass for secondary training to obtain the final coordinates of predicted position,which further improved the positioning accuracy.The experiment based on three-dimensional space of 0.8 m 0.8 m 0.8 m was carried out,and the results show that the average positioning error of the proposed algorithm is 3.22 cm,and the probability of positioning error less than 6 cm is 90%,which improves the positioning accuracy by 7.5%compared with the SSA-Elman algorithm and by 16%compared with the Elman network algorithm.

关 键 词：可见光 室内位置感知 麻雀搜索算法 ELMAN神经网络 K-MEANS聚类算法 

分 类 号：TN929.1[电子电信—通信与信息系统]