  For localization and mapping of indoor environments through WiFi signals,
locations are often represented as likelihoods of the received signal strength
indicator. In this work we compare various measures of distance between such
likelihoods in combination with different methods for estimation and
representation. In particular, we show that among the considered distance
measures the Earth Mover's Distance seems the most beneficial for the
localization task. Combined with kernel density estimation we were able to
retain the topological structure of rooms in a real-world office scenario.
