  Mobile device localization in wireless sensor networks is a challenging task.
It has already been addressed when the WiFI propagation maps of the access
points are modeled deterministically. However, this procedure does not take
into account the environmental dynamics and also assumes an offline human
training calibration. In this paper, the maps are made of an average indoor
propagation model combined with a perturbation field which represents the
influence of the environment. This perturbation field is embedded with a prior
distribution. The device localization is dealt with using Sequential Monte
Carlo methods and relies on the estimation of the propagation maps. This
inference task is performed online, i.e. using the observations sequentially,
with a recently proposed online Expectation Maximization based algorithm. The
performance of the algorithm are illustrated through Monte Carlo experiments.
