Indoor Localization Using Neural Networks with Location Fingerprints

Authors:
Christos Laoudias;Demetrios G. Eliades;Paul Kemppi;Christos G. Panayiotou;Marios M. Polycarpou
Affiliations:
KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678;KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678;VTT Technical Research Centre of Finland, Espoo, Finland FIN-02044;KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678;KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

Reliable localization techniques applicable to indoor environments are essential for the development of advanced location aware applications. We rely on WLAN infrastructure and exploit location related information, such as the Received Signal Strength (RSS) measurements, to estimate the unknown terminal location. We adopt Artificial Neural Networks (ANN) as a function approximation approach to map vectors of RSS samples, known as location fingerprints, to coordinates on the plane. We present an efficient algorithm based on Radial Basis Function (RBF) networks and describe a data clustering method to reduce the network size. The proposed algorithm is practical and scalable, while the experimental results indicate that it outperforms existing techniques in terms of the positioning error.