  This paper deals with the problem of clustering data returned by a radar
sensor network that monitors a region where multiple moving targets are
present. The network is formed by nodes with limited functionalities that
transmit the estimates of target positions (after a detection) to a fusion
center without any association between measurements and targets. To solve the
problem at hand, we resort to model-based learning algorithms and instead of
applying the plain maximum likelihood approach, due to the related
computational requirements, we exploit the latent variable model coupled with
the expectation-maximization algorithm. The devised estimation procedure
returns posterior probabilities that are used to cluster the huge amount of
data collected by the fusion center. Remarkably, we also consider challenging
scenarios with an unknown number of targets and estimate it by means of the
model order selection rules. The clustering performance of the proposed
strategy is compared to that of conventional data-driven methods over synthetic
data. The numerical examples point out that the herein proposed solutions can
provide reliable clustering performance overcoming the considered competitors.
