Siegel distance-based covariance matrix selection for Space-Time Adaptive

Marc Oudin (1, 2, 3, 4) , Jean-Pierre Delmas (5, 1, 2, 4) , Frédéric Barbaresco (3)

1 TSP - CITI - Communications, Images et Traitement de l'Information
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 Thales Air Systems
4 CNRS - Centre National de la Recherche Scientifique
5 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications

This paper presents a new criterion to deal with training data heterogeneity in space-time adaptive processing (STAP). It is based on Siegel measure, which is a distance in the space of Hermitian positive definite matrices, and consists in computing the distance between the covariance matrix estimated from training data and an a priori matrix. After deriving a test based on this distance, the statistical behavior of Siegel distance is analyzed, as a function of the number of training samples. Its probability density function (PDF) is derived and related to that of the generalized inner product (GIP) in the case of one single training snapshot. Finally, simulations are performed to illustrate the interest of preprocessing training data using Siegel distance, in terms of STAP signal to interference plus noise ratio (SINR) performance

Mots clés: STAP; Siegel distance; Generalized Inner Product

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

Marc Oudin, Jean-Pierre Delmas, Frédéric Barbaresco. Siegel distance-based covariance matrix selection for Space-Time Adaptive. RADAR 2009 : International Radar Conference "Surveillance for a safer world", Oct 2009, Bordeaux, France. ⟨hal-00444661⟩