An Enhanced Indoor Localization System Using Crowdsourced Multi-Source Measurements

被引:0
|
作者
Yang, Biheng [1 ]
Li, Bin [1 ]
Yang, Lyuxiao [1 ]
Wu, Nan [1 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China
来源
2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC) | 2020年
基金
美国国家科学基金会;
关键词
indoor localization; multi-source measurements; crowdsourcing; clustering; hidden Markov model; HIDDEN MARKOV-MODELS;
D O I
10.1109/iccc49849.2020.9238861
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the rapid development of the mobile Internet, applications based on indoor localization have received increasing attention. In recent years, WiFi received signal strength (RSS) is widely used in indoor localization for the universally available WiFi infrastructure. However, the WiFi signal could easily be affected by non-line-of-sight and multipath propagation, which reduces the localization accuracy. In this paper, we propose an enhanced indoor localization system using multi-source measurements including WiFi RSS, ultra wideband (UWB) ranging, and inertial sensors to improve the performance. The multi-source measurements collected by users' smartphones are used for site survey in our system. To recover users' trajectories, we propose a crowdsourcing method to construct radio map. Moreover, a reference point clustering approach is used to improve system efficiency. A two-step localization method is proposed to locate a user. Experimental results show that the proposed system achieves better performance than onlyWiFi-based or UWB-based method.
引用
收藏
页码:788 / 793
页数:6
相关论文
共 50 条
  • [1] Multi-Source Data Fusion Method for Indoor Localization System
    Cui, Jishi
    Li, Bin
    Yang, Lyuxiao
    Wu, Nan
    2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2020, : 29 - 33
  • [2] Human Localization Using Multi-Source Heterogeneous Data in Indoor Environments
    Sun, Yongliang
    Meng, Weixiao
    Li, Cheng
    Zhao, Nan
    Zhao, Kanglian
    Zhang, Naitong
    IEEE ACCESS, 2017, 5 : 812 - 822
  • [3] Source Isolation Measurements in a Multi-Source Coupled System
    Patnaik, Abhishek
    Shen, Guangyao
    Pommerenke, David
    Boettcher, Martin
    Aichele, Herman
    Keller, Christoph
    Khilkevich, Victor
    2017 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY & SIGNAL/POWER INTEGRITY (EMCSI), 2017, : 75 - 80
  • [4] Person Localization using Machine Learning in Multi-Source Camera Surveillance System
    Nabil, Mahmoud
    Sherif, Ahmed
    Mahmoud, Mohamed
    Alsmary, Waleed
    Alsabaan, Maazen
    SOUTHEASTCON 2022, 2022, : 110 - 116
  • [5] Multi-source localization by genetic algorithm using MEG
    Nagano, T
    Ohno, Y
    Uesugi, N
    Ikeda, H
    Ishiyama, A
    Kasai, N
    IEEE TRANSACTIONS ON MAGNETICS, 1998, 34 (05) : 2976 - 2979
  • [6] Multi-source localization by using offset residual weight
    Jia, Maoshen
    Gao, Shang
    Bao, Changchun
    EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 2021, 2021 (01)
  • [7] Multi-source localization by using offset residual weight
    Maoshen Jia
    Shang Gao
    Changchun Bao
    EURASIP Journal on Audio, Speech, and Music Processing, 2021
  • [8] Hybrid Indoor Tracking Using Crowdsourced Measurements
    Vatansever, Zafer
    Brandt-Pearce, Maite
    2017 26TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN 2017), 2017,
  • [9] WI-LO: Wireless Indoor LOcalization through Multi-Source Radio Fingerprinting
    Di Felice, Marco
    Bocanegra, Carlos
    Chowdhury, Kaushik Roy
    2018 10TH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS & NETWORKS (COMSNETS), 2018, : 305 - 311
  • [10] Indoor localization method based on pedestrian dead reckoning aided by multi-source fusion
    Liu C.-Y.
    Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology, 2016, 24 (02): : 208 - 214