  Multiple-stage adaptive architectures are conceived to face with the problem
of target detection buried in noise, clutter, and intentional interference.
First, a scenario where the radar system is under the electronic attack of
noise-like interferers is considered. In this context, two sets of training
samples are jointly exploited to devise a novel two-step estimation procedure
of the interference covariance matrix. Then, this estimate is plugged in the
adaptive matched filter to mitigate the deleterious effects of the noise-like
jammers on radar sensitivity. Besides, a second scenario, which also includes
the presence of coherent jammers, is addressed. Specifically, the sparse nature
of data is brought to light and the compressive sensing paradigm is applied to
estimate target response and coherent jammers amplitudes. The likelihood ratio
test, where the unknown parameters are replaced by previous estimates, is
designed and assessed. Remarkably, the sparse approach allows for echo
classification and estimation of both angles of arrival and number of the
interfering sources. The performance analysis, conducted resorting to simulated
data, highlights the effectiveness of the newly proposed architectures also in
comparison with suitable competing architectures (when they exist).
