  Monitoring interferences to satellite-based navigation systems is of
paramount importance in order to reliably operate critical infrastructures,
navigation systems, and a variety of applications relying on satellite-based
positioning. This paper investigates the use of crowdsourced data to achieve
such detection and monitoring at a central node that receives the data from an
arbitrary number of agents in an area of interest. Under ideal conditions, the
pathloss model is used to compute the Cram\'er-Rao Bound of accuracy as well as
the corresponding maximum likelihood estimator. However, in real scenarios
where obstructions and reflections are common, the signal propagation is far
more complex than the pathloss model can explain. We propose to augment the
pathloss model with a data-driven component, able to explain the complexities
of the propagation channel. The paper shows a general methodology to jointly
estimate the interference location and the parameters of the augmented model,
showing superior performances in complex scenarios such as those encountered in
urban environments.
