Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Localization using neural networks in wireless sensor networks
Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications
Localization of mobile users using trajectory matching
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Indoor geolocation science and technology
IEEE Communications Magazine
Towards planet-scale localization on smartphones with a partial radiomap
Proceedings of the 4th ACM international workshop on Hot topics in planet-scale measurement
Indoor geolocation on multi-sensor smartphones
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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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.