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In this paper, we propose three Low-Rank (LR) STAP Filters and the corresponding adaptive detectors when the disturbance is modelled as the sum of a Spherically Invariant Random Vector (SIRV) and a white noise Gaussian. The orthogonal projectors onto the subspace clutter are built from the Sample Covariance Matrix (SCM), the persymmetric SCM (by using the temporal and/or spatial symmetric properties of the RADAR system) and the Normalized Sample Covariance Matrix (NSCM). We present the theoretical performances of these three filters based on the statistical law of Signal to Noise Ratio (SNR) Loss computedfrom an analysis of perturbation. We also study the robustness of these filters to the secondary data contamination by target components. These different properties are finally checked on the data DGA/MI.
RÉSUMÉ
Dans ce papier, nous proposons trois filtres STAP rang faible et les détecteurs adaptatifs correspondants dans le cadre d’un bruit composé d’un clutter rang faible de distribution Spherically Invariant Random Vector (SIRV) plus d’un bruit blanc Gaussien. Les projecteurs orthogonaux au sous espace fouillis sont construits à partir de la Sample Covariance Matrix (SCM), de la SCM persymétrique (utilisant les propriétés de symétrie spatiales et temporelles du RADAR) et de la Normalized Sample Covariance Matrix (NSCM). Nous présentons les performances de ces trois filtres en terme de pertes en Rapport Signal à Bruit (RSB) à l’aide d’une analyse de perturbation. Nous étudions aussi la robustesse de ces filtres STAP à la contamination des données secondaires par des composants de la cible. Ces différentes propriétés sont finalement vérifiées sur les données CELAR.
filtering, low-rank, SIRV, Normalized Sample Covariance Matrix, persymmetry
MOTS-CLÉS
filtrage, rang faible, SIRV, Normalized Sample Covariance Matrix, persymétrie
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