Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

作     者：蒋志平 惠维 李向阳 唐少杰 赵季中 韩劲松 赵鲲 王志 肖波 Zhi-Ping Jiang;Wei Xi;Xiangyang Li;Shao jie Tang;Ji-Zhong Zhao;Jin-Song Han;Kun Zhao;Zhi Wang;Bo Xiao

作者机构：School of Electronic and Information Engineering Xi’an Jiaotong University CCF ACM IEEE Department of Computer Science Illinois Institute of Technology Chicago IL 60616 U.S.A. Department of Information Science University of Texas at Dallas Richardson TX U.S.A. Network Center The Aviation University of Air Fone 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2014年第29卷第4期

页      面：589-604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] 081802[工学-地球探测与信息技术] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 0818[工学-地质资源与地质工程] 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] 0811[工学-控制科学与工程] 

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.61325013,61190112,61170216,and 61228202 the Natural Science Foundation of USA under Grant Nos.CNS-0832120,CNS-1035894,ECCS-1247944,and ECCS-1343306 the Fundamental Research Funds for the Central Universities of China under Project No.2012jdgz02(Xi’an Jiaotong University) the Research Fund for the Doctoral Program of Higher Education of China under Project No.20130201120016 

主　　题：channel state information speed estimation fingerprint map graph matching indoor localization 

摘      要：Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location- based service （LBS）. Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information （CSI） based indoor localization scheme, C2IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points （APs）. C2IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength （RSS） fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C2IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.