MIMO Filters based on Robust Rank-Constrained Kronecker Covariance Matrix Estimation
| Autor: | Arnaud Breloy, Yongchan Gao, Frédéric Pascal, Guillaume Ginolhac |
|---|---|
| Přispěvatelé: | Laboratoire Energétique Mécanique Electromagnétisme (LEME), Université Paris Nanterre (UPN), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Xidian University, Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-17-ASTR-0015,MARGARITA,Nouvelles Techniques Robustes et d'Inférences pour le Radar Adaptatif Moderne(2017) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adaptive signal processing
Rank (linear algebra) Kronecker product Computer science MIMO Array processing 02 engineering and technology Majorization-Minimization symbols.namesake Kronecker delta 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering covariance matrix Covariance matrix Estimator 020206 networking & telecommunications low-rank filters Robust estimation Additive white Gaussian noise Control and Systems Engineering Signal Processing symbols 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Software |
| Zdroj: | Signal Processing Signal Processing, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩ Signal Processing, Elsevier, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩ |
| ISSN: | 0165-1684 1872-7557 |
| DOI: | 10.1016/j.sigpro.2021.108116⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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