  In this paper, we address the problem of classifying clutter returns in order
to partition them into statistically homogeneous subsets. The classification
procedure relies on a model for the observables including latent variables that
is solved by the expectation-maximization algorithm. The derivations are
carried out by accounting for three different cases for the structure of the
clutter covariance matrix. A preliminary performance analysis highlights that
the proposed technique is a viable means to cluster clutter returns over the
range.
