In past few years lot of attention was attracted to Location Based Services (LBS) [1, 2]. These services are used not only in transport systems, but have also importance in applications in indoor environment. Since the basic requirement for LBS is knowledge of user position, novel localization algorithms, which can be used in indoor environment, have to be developed. These algorithms are mostly based on radio networks [4], e.g. Wi-Fi, GSM, Bluetooth, because satellite navigation systems e.g. GPS can not reliable work in indoor environment due to high signal attenuations. In this work algorithms based on Wi-Fi networks were used. Main advantage is almost ubiquitous Wi-Fi coverage in indoor environment and implementation of Wi-Fi receivers into almost all devices. So there is no need to develop any additional hardware. In this work fingerprinting algorithms are used to estimate position of mobile device. Fingerprinting algorithms utilize information about Received Signal Strength (RSS) and do not need to know, position of Access Points (APs). Main advantage of fingerprinting algorithms is that they seem to be immune to multipath propagation, which is very strong in indoor environment. On the other hand, drawback of this method seems to be calibration (offline) phase, where time consuming measurements of radio map takes place. It is clear that RSS fluctuations have impact on localization accuracy. For this reason at least 20 samples of RSS should be measured on each position [5] (in both online and offline phase) and fingerprint is calculated from these samples. Since there are many ways how to process measured RSS data into fingerprint, optimal solution will be found using simulations. The rest of the paper is organized as follows. In next section fingerprinting localization algorithms used in simulation will be described in detail. In section three different methods of fingerprint processing will be introduced. Section four describes the simulation model and simulation scenario. Results of the simulations will be shown in section five. Section six concludes the paper.
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