An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion

作     者：Haoyu Yang Yuanshuo Wang Dongchen Li Tiancheng Li Haoyu Yang;Yuanshuo Wang;Dongchen Li;Tiancheng Li

作者机构：the Key Laboratory of Information Fusion Technology(Ministry of Education)School of AutomationNorthwest-ern Polytechnical UniversityXi’an 710072China CSSC Systems Engineering Research InstituteBeijing 100094China 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2022年第31卷第6期

页      面：570-583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：partially supported by the National Natural Science Foun-dation of China(No.62071389) 

主　　题：3D indoor localization fingerprint fusion positioning time-difference of arrival pedestrian dead reckoning received signal strength 

摘      要：In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D *** FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization *** also introduce auxiliary parameters to estimate the target motion ***,we can locate the static pedestrians and track the the moving *** the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the *** compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D *** results corroborate the effectiveness of our proposed approach.