About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Workshop Indoor/outdoor Location Based Services

Research Article

Selective Mixture of Gaussians Clustering for Location Fingerprinting

Download1100 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/icst.mobiquitous.2014.257983,
        author={Khuong Nguyen and Zhiyuan Luo},
        title={Selective Mixture of Gaussians Clustering for Location Fingerprinting},
        proceedings={Workshop Indoor/outdoor Location Based Services},
        publisher={ICST},
        proceedings_a={I-LOCATE},
        year={2014},
        month={11},
        keywords={location fingerprinting clustering mixture of gaussians},
        doi={10.4108/icst.mobiquitous.2014.257983}
    }
    
  • Khuong Nguyen
    Zhiyuan Luo
    Year: 2014
    Selective Mixture of Gaussians Clustering for Location Fingerprinting
    I-LOCATE
    ICST
    DOI: 10.4108/icst.mobiquitous.2014.257983
Khuong Nguyen1,*, Zhiyuan Luo1
  • 1: Royal Holloway, University of London
*Contact email: khuong@cantab.net

Abstract

One of the challenges of location fingerprinting to be deployed in the real offices is the training database handling process, which does not scale well with increasing amount of tracking space to be covered. However, little attention was paid to tackle such issue, where the majority of previous work rather focused on improving the tracking accuracy. In this paper, we propose a novel idea to enhance fingerprinting's processing speed and positioning accuracy with mixture of Gaussians clustering. We realised the key difference between fingerprinting and other un-supervised problems, that is we do know the label (the Cartesian co-ordinate) of the signal data in advance. This key information was largely ignored in previous work, where the fingerprinting clustering was based solely on the signal data information. By exploiting this information, we tackle the indoor signal multipath and shadowing with two-level signal data clustering and Cartesian co-ordinate clustering. We tested our approach in a real office environment with harsh indoor condition, and concluded that our clustering scheme does not only reduce the fingerprinting processing time, but also improves the positioning accuracy.

Keywords
location fingerprinting clustering mixture of gaussians
Published
2014-11-17
Publisher
ICST
http://dx.doi.org/10.4108/icst.mobiquitous.2014.257983
Copyright © 2014–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL