Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection

By Ernesto Conte, Antonio De Maio, Giuseppe Ricci. IEEE Transactions on Signal Processing, 2002

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Impact of Sea Clutter Nonstationarity on Disturbance Covariance Matrix Estimation and CFAR Detector Performance (2010, methodology): “Substituting M̂SCM into (2) we obtain a detector that is CFAR only with respect to Mx and whose probability of false alarm (PFA) depends on the texture probability density function (pdf) [11, 4, 6].”
Covariance Structure Maximum-Likelihood Estimates in Compound Gaussian Noise: Existence and Algorithm Analysis (2008, methodology): “It has been shown in [12] and [13] that estimation schemes developed under both the stochastic case (Section II-A) and the deterministic case (Section II-B) lead to the analysis of the same equation [(2) and (6)], whose solution is a fixed point (FP) of (7).”
Complex Elliptically Symmetric Distributions: Survey, New Results and Applications (2012, background): “, in [17], [19], [33], [77] and [78]) belong to the class ofML- or -estimators of the scatter matrix, and thus the represented unified framework and theory herein directly apply for their statistical, asymptotic and computational analysis.”