First- and Second-Order Moments of the Normalized Sample Covariance Matrix of Spherically Invariant Random Vectors

Under Gaussian assumptions, the sample covariance matrix (SCM) is encountered in many covariance based processing algorithms. In case of impulsive noise, this estimate is no more appropriate. This is the reason why when the noise is modeled by spherically invariant random vectors (SIRV), a natural e...

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Bibliographic Details
Published in:IEEE signal processing letters 2007-06, Vol.14 (6), p.425-428
Main Authors: Bausson, S., Pascal, F., Forster, P., Ovarlez, J.-P., Larzabal, P.
Format: Article
Language:English
Subjects:
Applications
Closed-form solution
Clutter
Computer Science
Covariance
Covariance matrix
Eigenvalues and eigenfunctions
Estimates
Estimation
Fading
Gaussian
Invariants
Mathematical analysis
Matrix decomposition
Maximum likelihood estimation
Noise
normalized sample covariance matrix (NSCM)
Other Statistics
Performance analysis
Radar applications
Signal and Image Processing
Sonar
spherically invariant random vectors (SIRV)
Statistics
Vectors (mathematics)
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Summary:Under Gaussian assumptions, the sample covariance matrix (SCM) is encountered in many covariance based processing algorithms. In case of impulsive noise, this estimate is no more appropriate. This is the reason why when the noise is modeled by spherically invariant random vectors (SIRV), a natural extension of the SCM is extensively used in the literature: the well-known normalized sample covariance matrix (NSCM), which estimates the covariance of SIRV. Indeed, this estimate gets rid of a fluctuating noise power and is widely used in radar applications. The aim of this paper is to derive closed-form expressions of the first- and second-order moments of the NSCM
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2006.888400