Title :
Performance analysis of MDL criterion for the detection of noncircular or/and nonGaussian components
Author :
Delmas, Jean Pierre ; Meurisse, Yann
Author_Institution :
Dept. CITI, TELECOM SudParis, Evry, France
Abstract :
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.
Keywords :
covariance matrices; eigenvalues and eigenfunctions; performance evaluation; signal processing; MDL criterion; asymptotic analysis; asymptotic bias; closed-form expressions; covariance matrix; eigenvalue decomposition; eigenvectors; independent identically distributed nonnecessarily Gaussian data; independent identically distributed nonnecessarily circular data; minimum description length criterion; nonGaussian component detection; noncircular component detection; statistical performance analysis; variance; Approximation methods; Argon; Covariance matrix; Eigenvalues and eigenfunctions; Gaussian distribution; Performance analysis; Signal to noise ratio; Eigen value decomposition; eigenvalue; eigenvector; minimum description length; nonGaussian; noncircular; sample covariance matrix; source detection;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5947276
