Supplementary open dataset for WiFi indoor localization based on received signal strength

作     者：Jingxue Bi Yunjia Wang Baoguo Yu Hongji Cao Tongguang Shi Lu Huang 

作者机构：School of Surveying and Geo-InformaticsShandong Jianzhu UniversityJinan250101China School of Environment and Spatial InformaticsChina University of Mining and TechnologyXuzhou221116China Key Laboratory of Satellite Navigation System and Equipment TechnologyThe 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050081China 

出 版 物：《Satellite Navigation》 (卫星导航（英文）)

年 卷 期：2022年第3卷第3期

页      面：178-192,I0005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：National Natural Science Foundation of China(No.42001397) National Key Research and Development Program of China(No.2016YFB0502102) Introduction and Training Program of Young Creative Talents of Shandong Province(No.0031802) Doctoral Research Fund of Shandong Jianzhu University(No.XNBS1985) National College Student Innovation and Entrepreneurship Training Program(No.S202110430036) 

主　　题：WiFi Indoor localization Open dataset RSS AP Machine learning 

摘      要：Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor ***,they can’t meet all the demands of WiFi RSS-based localization.A supplementary open dataset for WiFi indoor localization based on RSS,called as SODIndoorLoc,covering three buildings with multiple floors,is presented in this *** dataset includes dense and uniformly distributed Reference Points(RPs)with the average distance between two adjacent RPs smaller than 1.2 ***,the locations and channel information of pre-installed Access Points(APs)are summarized in the *** addition,computer-aided design drawings of each floor are *** SODIndoorLoc supplies nine training and five testing *** standard machine learning algorithms and their variants(eight in total)are explored to evaluate positioning accuracy,and the best average positioning accuracy is about 2.3 ***,the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent *** dataset can be used for clustering,classification,and regression to compare the performance of different indoor positioning applications based on WiFi RSS values,e.g.,high-precision positioning,building,floor recognition,fine-grained scene identification,range model simulation,and rapid dataset construction.