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 

机构地区：[1]School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China [2]Peng Cheng Laboratory,Shenzhen 518055,China [3]School of Information and Communications Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

出　　处：《China Communications》2023年第2期139-154,共16页中国通信（英文版）

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)。

摘　　要：High-precision localization technology is attracting widespread attention in harsh indoor environments.In 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 system.In 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 precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The 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.

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

分 类 号：TN925[电子电信—通信与信息系统]