Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010

AUTHORS

Iness Ahriz, Yacine Oussar, Bruce Denby, Gérard Dreyfus

ABSTRACT

Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements. Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared. The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used. To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance. More... »

PAGES

1-7

References to SciGraph publications

  • 1977-12. Restart procedures for the conjugate gradient method in MATHEMATICAL PROGRAMMING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1155/2010/497829

    DOI

    http://dx.doi.org/10.1155/2010/497829

    DIMENSIONS

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