Performance analysis of MDL criterion for the detection of noncircular or/and nonGaussian components

Jean-Pierre Delmas (1, 2, 3, 4) , Yann Meurisse (2)

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

This paper presents an asymptotic analysis of the eigen value decomposition (EVD) of the sample covariance matrix associated with independent identically distributed (IID) non necessarily circular and Gaussian data that extends the well known analysis presented in the literature for circular and Gaussian data. Closed-form expressions of the asymptotic bias and variance of the sample eigenvalues and eigenvectors are given. As an application of these extended expressions, the statistical performance analysis of the minimum description length (MDL) criterion applied to the detection of the number of noncircular or/and nonGaussian components is considered

Mots clés: Eigen value decomposition; Sample covariance matrix; Eigenvalue; Eigenvector; Minimum description length; Source detection; Noncircular; NonGaussian

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

Jean-Pierre Delmas, Yann Meurisse. Performance analysis of MDL criterion for the detection of noncircular or/and nonGaussian components. ICASSP 2011 : 36th International Conference on Acoustics, Speech and Signal Processing, May 2011, Prague, Czech Republic. pp.4188-4191, ⟨10.1109/ICASSP.2011.5947276⟩. ⟨hal-01302426⟩