  The spherically invariant random process (SIRP) clutter model is commonly
used in scenarios where the radar clutter cannot be correctly modeled as a
Gaussian process. In this short communication, we devise a novel
Maximum-Likelihood (ML)-based iterative estimator for direction-of-departure
and direction-of-arrival estimation in the Multiple-input multiple-output
(MIMO) radar context in the presence of SIRP clutter. The proposed estimator
employs a stepwise numerical concentration approach w.r.t. the objective
function related to the marginal likelihood of the observation data. Our
estimator leads to superior performance, as our simulations show, w.r.t. to the
existing likelihood based methods, namely, the conventional, the conditional
and the joint likelihood based estimators, and w.r.t. the robust subspace
decomposition based methods. Finally, interconnections and comparison between
the Iterative Marginal ML Estimator (IMMLE), Iterative Joint ML Estimator
(IJMLE) and Iterative Conditional ML Estimator (ICdMLE) are provided.
