Extreme learning machine for wireless indoor localization

  • Authors:

  • Wendong Xiao;Peidong Liu;Wee-Seng Soh;Yunye Jin

  • Affiliations:

  • University of Science and Technology Beijing, Beijing, China;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;Institute for Infocomm Research, A*STAR, Singapore, Singapore

  • Venue:
  • Proceedings of the 11th international conference on Information Processing in Sensor Networks
  • Year:
  • 2012

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Abstract

Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices.