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The demand for location-based services (LBS) in indoor environments such as shopping malls and airports has increased recently. In order to support such LBS applications accurate indoor localization systems are required. Therefore, in this paper, K-Nearest Neighbor (K-NN) and Random Decision Forest (RDF) algorithms for GSM RSS based RF fingerprinting method are presented in order find the location of mobile users in indoor environments. For studying the performance of these two algoritms in realistic indoor environments, a measurement campaign is conducted in Istanbul AtaSehir Palladium shopping mall using GSM cellular networks. The location estimation error performance of these two algoritms are obtained in the form of CDF results by using the collected GSM RSS data. Moreover, the effects of different mobile phone brands (Sony Ericsson and Nokia) on the location estimation error performance are investigated using the measurement data. According to the results, RDF method performs slightly better than K-NN method. Additionally, Sony Ericsson mobile phone provides better location estimation performance than that of Nokia mobile phone. |
