  In this paper, we address the problem of classifying data within the radar
reference window in terms of statistical properties. Specifically, we partition
these data into statistically homogeneous subsets by identifying possible
clutter power variations with respect to the cells under test (accounting for
possible range-spread targets) and/or clutter edges. To this end, we consider
different situations of practical interest and formulate the classification
problem as multiple hypothesis tests comprising several models for the
operating scenario. Then, we solve the hypothesis testing problems by resorting
to suitable approximations of the model order selection rules due to the
intractable mathematics associated with the maximum likelihood estimation of
some parameters. Remarkably, the classification results provided by the
proposed architectures represent an advanced clutter map since, besides the
estimation of the clutter parameters, they contain a clustering of the range
bins in terms of homogeneous subsets. In fact, such information can drive the
conventional detectors towards more reliable estimates of the clutter
covariance matrix according to the position of the cells under test. The
performance analysis confirms that the conceived architectures represent a
viable means to recognize the scenario wherein the radar is operating at least
for the considered simulation parameters.
