Iterative Marginal Maximum Likelihood DOD and DOA Estimation for MIMO Radar in the Presence of SIRP Clutter

Bruno Meriaux (1) , Xin Zhang , Mohammed Nabil El Korso (2) , Marius Pesavento (3)

1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
2 LEME - Laboratoire Energétique Mécanique Electromagnétisme
3 Darmstadt University of Technology [Darmstadt]

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.

Mots clés: maximum likelihood estimation; spherically invariant random process; MIMO radar

Domaines: Traitement du signal et de l'image [eess.SP]

Bruno Meriaux, Xin Zhang, Mohammed Nabil El Korso, Marius Pesavento. Iterative Marginal Maximum Likelihood DOD and DOA Estimation for MIMO Radar in the Presence of SIRP Clutter. Signal Processing, 2019, 155, pp.384-390. ⟨10.1016/j.sigpro.2018.09.034⟩. ⟨hal-01888650v2⟩