engleski

RBF Network Design for WLAN Indoor Positioning

To provide context-aware services and applications, key issue is to enable accurate estimation of user location. Localization methods based on Received Signal Strength (RSS) fingerprints in WLANs are gaining huge interest as localization solution, where, as pattern matching algorithm different methods are used. In this paper we investigate the usage of Radial Basis Function (RBF) neural network as approximation function that maps RSS fingerprints to user locations. We provide detailed analysis on network training performance considering different number of neurons and radial basis functions' spread values. We evaluated the developed positioning system in real world WLAN indoor environment and obtained good positioning results (mean error of 2.3 m).

Localization; Received Signal Strength (RSS); fingerprinting; WLAN; RBF

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