Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems are still unsatisfactory for many applications, due to the difficult modeling of the propagation channel, whose features are affected by severe changes. In this paper we propose a localization algorithm which takes into account the nonstationarity of the working conditions by estimating and tracking the key parameters of RSS propagation. It is based on a Sequential Monte Carlo realization of the optimal Bayesian estimation scheme, whose functioning is improved by exploiting the Rao-Blackwellization rationale. Two key statistical models for RSS characterization are deeply analyzed, by presenting effective implementations of the proposed scheme and by assessing the positioning accuracy by extensive computer experiments. Many different working conditions are analyzed by simulated data and corroborated through the validation in a real world scenario.
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