  In this paper, four adaptive radar architectures for target detection in
heterogeneous Gaussian environments are devised. The first architecture relies
on a cyclic optimization exploiting the Maximum Likelihood Approach in the
original data domain, whereas the second detector is a function of transformed
data which are normalized with respect to their energy and with the unknown
parameters estimated through an Expectation-Maximization-based alternate
procedure. The remaining two architectures are obtained by suitably combining
the estimation procedures and the detector structures previously devised.
Performance analysis, conducted on both simulated and measured data, highlights
that the architecture working in the transformed domain guarantees the constant
false alarm rate property with respect to the interference power variations and
a limited detection loss with respect to the other detectors, whose detection
thresholds nevertheless are very sensitive to the interference power.
