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

40 Citations (Scopus)

Abstract

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
PublisherInstitute of Electrical and Electronic Engineers IEEE
Number of pages6
ISBN (Print)978-1-4244-4148-8
DOIs
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

Conference

ConferenceIEEE Global Telecommunications Conference, GLOBECOM 2009
Abbreviated titleGLOBECOM 2009
CountryUnited States
CityHonolulu
Period30/11/094/12/09

Fingerprint

Radial basis function networks
Wireless local area networks (WLAN)
Computational complexity
Satellites
Neural networks

Keywords

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

Cite this

Laoudias, C., Kemppi, P., & Panayiotou Christos, G. (2009). Localization using radial basis function networks and signal strength fingerprints in WLAN. In Proceedings of GLOBECOM 2009: 2009 IEEE Global Telecommunications Conference Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/GLOCOM.2009.5425278
Laoudias, Christos ; Kemppi, Paul ; Panayiotou Christos, G. / Localization using radial basis function networks and signal strength fingerprints in WLAN. Proceedings of GLOBECOM 2009: 2009 IEEE Global Telecommunications Conference. Institute of Electrical and Electronic Engineers IEEE, 2009.
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Laoudias, C, Kemppi, P & Panayiotou Christos, G 2009, Localization using radial basis function networks and signal strength fingerprints in WLAN. in Proceedings of GLOBECOM 2009: 2009 IEEE Global Telecommunications Conference. Institute of Electrical and Electronic Engineers IEEE, IEEE Global Telecommunications Conference, GLOBECOM 2009, Honolulu, United States, 30/11/09. https://doi.org/10.1109/GLOCOM.2009.5425278

Localization using radial basis function networks and signal strength fingerprints in WLAN. / Laoudias, Christos; Kemppi, Paul; Panayiotou Christos, G.

Proceedings of GLOBECOM 2009: 2009 IEEE Global Telecommunications Conference. Institute of Electrical and Electronic Engineers IEEE, 2009.

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

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N2 - 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.

AB - 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.

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Laoudias C, Kemppi P, Panayiotou Christos G. Localization using radial basis function networks and signal strength fingerprints in WLAN. In Proceedings of GLOBECOM 2009: 2009 IEEE Global Telecommunications Conference. Institute of Electrical and Electronic Engineers IEEE. 2009 https://doi.org/10.1109/GLOCOM.2009.5425278