• DocumentCode
    260597
  • Title

    Efficient WiFi fingerprint training using semi-supervised learning

  • Author

    Ye Yuan ; Ling Pei ; Changqing Xu ; Qianchen Liu ; Tingyu Gu

  • Author_Institution
    Sch. of Electron. Inf. & Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
  • fYear
    2014
  • fDate
    20-21 Nov. 2014
  • Firstpage
    148
  • Lastpage
    155
  • Abstract
    Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce an efficient WiFi fingerprint training method: Fa-Fi namely fast fingerprint generation, which uses semi-supervised learning in this article. This proposed method can reduce the training phase time cost to about 1/5, and guarantee the localization accuracy at the same time.
  • Keywords
    RSSI; learning (artificial intelligence); radionavigation; telecommunication computing; vectors; wireless LAN; Wi-Fi fingerprint training method; Wi-Fi positioning approach; fast fingerprint generation; off-line training phase; radio map; received signal strength indication vector; semisupervised learning; training phase time cost; Accuracy; Databases; Fingerprint recognition; IEEE 802.11 Standards; Legged locomotion; Training; Vectors; Gaussian Processes; continuously sampling; fingerprint; indoor localization; semi-supervise learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2014
  • Conference_Location
    Corpus Christ, TX
  • Type

    conf

  • DOI
    10.1109/UPINLBS.2014.7033722
  • Filename
    7033722