基于深度神经网络的5G室内联合定位算法5G indoor hybrid positioning algorithm based on deep neural network
王军选;王漪楠;
摘要(Abstract):
为了有效地降低室内多变的传播环境带来的多径效应和非视距(non-line of sight,NLOS)的传播误差,提出一种在第五代移动通信技术(5th generation mobile communication technology,5G)室内小小区场景下,结合深度神经网络(deep neural network,DNN)和多天线的多信号分类算法(multiple signal classification,MUSIC)的联合室内定位方案。首先, MUSIC利用用户终端上传的通信系统数据估计到达角度(angle of arrival,AOA)。其次,将接收信号强度指示(radio signal strength indication,RSSI)数据输入离线阶段训练好的DNN模型中,估计用户终端的直线距离。最后,利用DNN和AOA算法将估计的信息输入联合定位系统中得到用户的二维坐标。仿真结果表明,该算法相比于路径损耗模型具有更小的定位误差,能够提升定位性能。
关键词(KeyWords): 室内定位;第五代移动通信技术;深度神经网络;多信号分类
基金项目(Foundation): 国家重大专项项目(ZX201703001012-005)
作者(Author): 王军选;王漪楠;
Email:
DOI: 10.13682/j.issn.2095-6533.2020.04.007
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