HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

作     者：Junchang Sun Rongyan Gu Shiyin Li Shuai Ma Hongmei Wang Zongyan Li Weizhou Feng Junchang Sun;Rongyan Gu;Shiyin Li;Shuai Ma;Hongmei Wang;Zongyan Li;Weizhou Feng

作者机构：School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221116China Peng Cheng LaboratoryShenzhen 518055China School of Information and Communications EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第2期

页      面：139-154页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported in part by the National Natural Science Foundation of China under Grant No.61771474 in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243 in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051 in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02 in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT) 

主　　题：Ultra-wideband(UWB) inertial measurement unit(IMU) fingerprints positioning NLoS identification estimated errors mitigation deep belief network(DBN) radial basis function(RBF) 

摘      要：High-precision localization technology is attracting widespread attention in harsh indoor *** this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID *** particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization *** tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS ***,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization *** experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.