Robust rank constrained kronecker covariance matrix estimation

Arnaud Breloy (1) , Ying Sun (2) , P Babu (3) , Guillaume Ginolhac (4) , Daniel P. Palomar (3)

1 LEME - Laboratoire Energétique Mécanique Electromagnétisme
2 Center for Condensed Matter and Materials Physics, Department of Physics, Beihang University, Beijing
3 HKUST - Hong Kong University of Science and Technology
4 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.

Mots clés: Adaptive signal processing; covariance matrix estimation; low rank; robust estimation; Majorization-Minimization; Kronecker product

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

Arnaud Breloy, Ying Sun, P Babu, Guillaume Ginolhac, Daniel P. Palomar. Robust rank constrained kronecker covariance matrix estimation. 2016 50th Asilomar Conference on Signals, Systems and Computers, Nov 2016, Pacific Grove, United States. pp.810 - 814, ⟨10.1109/ACSSC.2016.7869159⟩. ⟨hal-01503788⟩