Auto-calibration for device-diversity problem in an indoor localization system

Hung Nguyen Manh, Ching-Chun Huang, Hsiao Yi Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Recently, the techniques for indoor localization have become more and more important and play a critical role in many mobile applications. Among them, the fingerprint-based indoor localization system has been recognized as a possible right way toward success. However, some challenges still remain. One issue should be addressed is the device diversity problem, where different devices would receive different radio signal strengths (RSS) at the same location. This problem breaks the fingerprint assumption - each location has its singular RSS. Conventional calibration methods require manually collecting pair-wise RSS data among devices to train the calibration model. To reduce human load, we proposed a method that could automatically calibrate the device diversity problem in an efficient way.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages78-79
Number of pages2
ISBN (Electronic)9781479987443
DOIs
StatePublished - 20 Aug 2015
Event2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 - Taipei, Taiwan
Duration: 6 Jun 20158 Jun 2015

Publication series

Name2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015

Conference

Conference2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
CountryTaiwan
CityTaipei
Period6/06/158/06/15

Keywords

  • Calibration
  • Fingerprint recognition
  • IEEE 802.11 Standard
  • Mobile handsets
  • Sensors
  • Testing
  • Training

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