Asymptotically minimum variance estimator in the singular case

Habti Abeida (1, 2, 3) , Jean-Pierre Delmas (1, 2, 3)

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

This paper addresses asymptotically (in the number of measurements) minimum variance (AMV) estimators within the class of estimators based on a mixture of real and complex-valued sequence of statistics whose first covariance of its asymptotic distribution is singular. Thanks to two conditions, we extend the standard AMV estimator. We prove that these conditions are satisfied for the estimates of orthogonal projection matrices used in subspace-based algorithms. Finally, we illustrate our findings for subspace-based algorithms in the DOA estimation for complex noncircular signals.

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

Habti Abeida, Jean-Pierre Delmas. Asymptotically minimum variance estimator in the singular case. EUSIPCO 2005 : 13th European Signal Processing Conference, Sep 2005, Antalya, Turkey. ⟨hal-00446573⟩