Localization using radial basis function networks and signal strength fingerprints in WLAN

Christos Laoudias, Paul Kemppi, G. Panayiotou Christos

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

    65 Citations (Scopus)


    Fingerprinting localization techniques provide reliable location estimates and enable the development of location aware applications especially for indoor environments, where satellite based positioning is infeasible. In our approach we utilize Received Signal Strength (RSS) fingerprints collected in known locations and employ a Radial Basis Function (RBF) neural network to approximate the function that maps fingerprints to location coordinates. We present a clustering scheme to reduce the size and computational complexity of the RBF architecture and demonstrate the applicability of this approach in a real-world WLAN setup. Experimental results indicate that the RBF based method is an efficient approach to the location determination problem that outperforms existing techniques in terms of the positioning error.
    Original languageEnglish
    Title of host publicationProceedings of GLOBECOM 2009
    Subtitle of host publication2009 IEEE Global Telecommunications Conference
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Print)978-1-4244-4148-8
    Publication statusPublished - 2009
    MoE publication typeA4 Article in a conference publication
    EventIEEE Global Telecommunications Conference, GLOBECOM 2009 - Honolulu, United States
    Duration: 30 Nov 20094 Dec 2009


    ConferenceIEEE Global Telecommunications Conference, GLOBECOM 2009
    Abbreviated titleGLOBECOM 2009
    Country/TerritoryUnited States


    • fingerprinting
    • localization
    • Radial Basis Function Networks
    • received signal strength
    • WLAN


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