基于KF-LSTM的UWB室内定位算法  

UWB indoor localization algorithm based on KF-LSTM

作　　者：田亚林 连增增[1] 王鹏辉 王孟奇 陆力 TIAN Yalin;LIAN Zengzeng;WANG Penghui;WANG Mengqi;LU Li(School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China;China Railway Sixth Survey and Design Institute Group Co.,Ltd.,Tianjin 300308,China)

机构地区：[1]河南理工大学测绘与国土信息工程学院,河南焦作454000 [2]中铁第六勘察设计院集团有限公司,天津300308

出　　处：《测绘通报》2024年第7期95-99,151,共6页Bulletin of Surveying and Mapping

基　　金：河南省高校基本科研业务费专项(NSFRF230405);河南理工大学2017年度博士基金(B2017-10);河南理工大学青年骨干教师资助计划(2022XQG-08);河南省自然科学基金(202300410180);国家自然科学基金(42374029)。

摘　　要：作为一种新型无线定位技术,超宽带在室内定位领域中引起了广泛关注。为了提高超宽带的定位精度,本文结合卡尔曼滤波和LSTM网络的优势,提出一种融合卡尔曼滤波的长短期记忆神经网络(KF-LSTM)算法。首先,通过卡尔曼滤波对UWB时序数据进行处理,削弱数据中的高斯白噪声;然后,将数据投入LSTM网络中进行训练,利用LSTM网络处理时序特征的优势处理非高斯噪声,进而得到更准确的标签位置。实测数据表明,与BP、KF-BP和LSTM网络算法相比,KF-LSTM算法的平均定位精度分别提高了70.21%、37.28%和38.23%,且KF-LSTM算法表现更稳定。As a new wireless localization technology,UWB has attracted much attention in the field of indoor localization.In order to improve localization accuracy in ultra-wide band,this paper combines the advantages of Kalman filtering and LSTM networks and proposes a long short-term memory neural network(KF-LSTM)algorithm that incorporates Kalman filtering.Firstly,the UWB timing data is processed by Kalman filtering to weaken the Gaussian white noise in the data,and then the data is put into the LSTM network for training,which takes advantage of the LSTM network's processing of timing features to deal with the non-Gaussian noise and then obtains a more accurate label location.The final measured data show that the average localization accuracy of the KF-LSTM algorithm is improved by 70.21%,37.28%and 38.23%compared to the BP,KF-BP and LSTM network algorithms respectively,and the KF-LSTM algorithm performs more stably.

关 键 词：超宽带 长短期记忆神经网络 卡尔曼滤波 室内定位 深度学习 

分 类 号：P228[天文地球—大地测量学与测量工程]