DocumentCode :
760670
Title :
Exact Maximum Likelihood Estimates for SIRV Covariance Matrix: Existence and Algorithm Analysis
Author :
Chitour, Yacine ; Pascal, Frédéric
Author_Institution :
Lab. des Signaux et Syst., Supelec, Gif-sur-Yvette
Volume :
56
Issue :
10
fYear :
2008
Firstpage :
4563
Lastpage :
4573
Abstract :
In this paper, we investigate the existence and the algorithm analysis of an adaptive scheme that has been introduced for covariance structure matrix estimation in the context of adaptive radar detection under non-Gaussian noise. This latter has been modeled by spherically invariant random vector (SIRV), which is the product c of the square root of a positive unknown random variable tau and an independent Gaussian vector x,c=radic(tau) x. A similar line of work was undertaken in the context of compound Gaussian noise, and this paper extends the previous results in the case of SIRV modeled noise. More precisely, the fixed-point estimate to be studied verifies a nonlinear algebraic equation (E)x=f(x). The aim of this paper is twofold. First, we prove that (E) admits a unique solution x; secondly, we show that the corresponding iterative algorithm xn+1=f(xn) converges to x for every admissible initial condition.
Keywords :
covariance matrices; iterative methods; maximum likelihood estimation; noise; signal processing; SIRV covariance matrix; SIRV modeled noise; adaptive radar detection; compound Gaussian noise; iterative algorithm; maximum likelihood estimation; nonGaussian noise; spherically invariant random vector; Adaptive detection; Fixed Point estimate; Maximum Likelihood estimate; SIRV model; adaptive detection; fixed-point estimate; iterative algorithm convergence; maximum likelihood estimate; spherically invariant random vector (SIRV) model;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2008.927464
Filename :
4547452
Link To Document :
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