engleski

RBF Network Design for Indoor Positioning Based on WLAN and GSM

Location-based services aim to improve the quality of everyday lives by enabling flexible and adaptive personal services and applications. In order 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 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 developed two real world indoor positioning systems in WLAN and GSM environment based on RBF neural networks. Compared to GSM based approach, WLAN system has the advantage in terms of lower localization error, but generally GSM signal coverage by far outreaches WLAN coverage and if less accurate positioning is required, GSM can also present a good solution.

Localization; Received Signal Strength (RSS); fingerprinting; RBF neural network; WLAN; GSM

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