MAXIMUM LIKELIHOOD ESTIMATION OF CLUTTER SUBSPACE IN NON HOMOGENEOUS NOISE CONTEXT

Arnaud Breloy (1) , Luc Le Magoarou (1) , Guillaume Ginolhac (2) , Frédéric Pascal (3) , Philippe Forster (1)

1 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie
2 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
3 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)

In the context of a disturbance composed of a Low Rank (LR) clutter plus a white Gaussian noise, the corresponding LR filters used to detect a target embedded in this disturbance needs less training vectors than classical methods to reach equivalent performance. Unlike the classical one which is based on covariance matrix of the noise, the LR filter is based on the clutter subspace projector. In this paper, we propose a new estimator of the clutter subspace projector for a disturbance composed of a LR Spherically Invariant Random Vectors (SIRV) plus a zero mean white Gaussian noise that does not require prior information on the SIRV's texture. Numerical simulations validate the introduced estimator, and its performance and robustness are tested on a Space Time Adaptive Processing (STAP) simulation.

Mots clés: Covariance Matrix and Projector estimation; Maximum Likelihood Estimator; Low-Rank clutter; SIRV; STAP filter

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

Arnaud Breloy, Luc Le Magoarou, Guillaume Ginolhac, Frédéric Pascal, Philippe Forster. MAXIMUM LIKELIHOOD ESTIMATION OF CLUTTER SUBSPACE IN NON HOMOGENEOUS NOISE CONTEXT. 21st European Signal Processing Conference (EUSIPCO 2013), Sep 2013, Marrakech, Morocco. pp.1-5. ⟨hal-00871219⟩