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Article Dans Une Revue Signal Processing Année : 2021

MIMO Filters based on Robust Rank-Constrained Kronecker Covariance Matrix Estimation

Résumé

In this paper, we propose a new estimator of the covariance matrix parameters when observations follow a mixture of a deterministic Compound-Gaussian (CG) and a white Gaussian noise. In particular, the covariance matrix of the CG contribution is assumed to be expressed as the Kronecker product of two low-rank matrices, which is a structure often involved in MIMO array processing. The proposed estimator is then obtained by maximizing the likelihood of the data with the use of a specifically tailored block Majorization-Minimization (MM) algorithm. Finally, the method is evaluated in terms of adaptive filtering on a MIMO-STAP radar setting, showing important improvements over standard processing.
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Dates et versions

hal-03273066 , version 1 (28-06-2021)

Identifiants

Citer

Arnaud Breloy, Guillaume Ginolhac, Yongchan Gao, Frédéric Pascal. MIMO Filters based on Robust Rank-Constrained Kronecker Covariance Matrix Estimation. Signal Processing, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩. ⟨hal-03273066⟩
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