Abstract
This correspondence presents an asymptotic analysis of the eigenvalue decomposition (EVD) of the sample covariance matrix associated with independent identically distributed (i.i.d.) 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 widely used minimum description length (MDL) criterion applied to the detection of the number of noncircular or/and non-Gaussian sources impinging on an array of sensors is considered with a particular attention paid to uncorrelated rectilinear sources.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
