  The joint adaptive detection of multiple point-like targets in scenarios
characterized by different clutter types is still an open problem in the radar
community. In this paper, we provide a solution to this problem by devising
detection architectures capable of classifying the range bins according to
their clutter properties and detecting possible multiple targets whose
positions and number are unknown. Remarkably, the information provided by the
proposed architectures makes the system aware of the surrounding environment
and can be exploited to enhance the entire detection and estimation performance
of the system. At the design stage, we assume three different signal models and
apply the latent variable model in conjunction with estimation procedures based
upon the expectation-maximization algorithm. In addition, for some models, the
maximization step cannot be computed in closed-form (at least to the best of
authors' knowledge) and, hence, suitable approximations are pursued, whereas,
in other cases, the maximization is exact. The performance of the proposed
architectures is assessed over synthetic data and shows that they can be
effective in heterogeneous scenarios providing an initial snapshot of the radar
operating scenario.
