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When dealing with impulsive background echoes, Gaussian model is no longer pertinent. We study in this paper the class of elliptically contoured (EC) distributions. They provide a multivariate location-scatter family of distributions that primarily serve as long tailed alternatives to the multivariate normal model. They are proven to represent a more accurate characterization of HSI data than models...
Hyperspectral data have been proved not to be multivariate normal but long tailed distributed. In order to take into account these features, the family of elliptical contoured distributions is proposed to describe noise statistical behavior. Although non-Gaussian models are assumed for background modeling and detectors design, the parameters estimation is still performed using classical Gaussian based...
In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical...
The Sample Covariance Matrix (SCM) is widely used in signal processing applications which require the estimation of the data covariance matrix. Indeed it exhibits good statistical properties and tractability. However its performance can become very bad in context of non-Gaussian signals, contaminated or missing data. In that case M-estimators provide a good alternative. They have been introduced within...
Standard covariance matrix estimation procedures can be very affected by either the presence of outliers in the data or some mismatch in their statistical model. In the Spherically Invariant Random Vectors (SIRV) framework, this paper proposes the statistical analysis of the Normalized Sample Covariance Matrix (NSCM) and the Fixed Point (FP) estimates in disturbances context. The main contribution...
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