In this paper, we address the problem of classifying clutter returns into statistically
homogeneous subsets. The classification procedures are devised assuming latent …

The performance of conventional direction-of-arrival (DOA) estimation methods degrades
greatly when there are few snapshots and the noise model is mismatched, especially in …

Many applications in signal processing require the estimation of mean and covariance
matrices of multivariate complex-valued data. Often, the data are non-Gaussian and are …

This article studies the statistical model of the non-centered mixture of scaled Gaussian
distributions (NC-MSG). Using the Fisher-Rao information geometry associated with this …

The estimation of covariance matrices is a core problem in many modern adaptive signal
processing applications. For matrix-and array-valued data, eg, MIMO communication …

When dealing with a parametric statistical model, a Riemannian manifold can naturally
appear by endowing the parameter space with the Fisher information metric. The geometry …

High‐breakdown‐point estimators of multivariate location and shape matrices, such as the
MM‐estimator with smoothed hard rejection and the Rocke S‐estimator, are generally …

We consider the problem of jointly estimating the location and scatter matrix of a Compound
Gaussian distribution with unknown deterministic texture parameters. When the location is …

Les radiotélescopes interféromètres permettent de reconstruire des images des émissions
radio provenant de sources célestes. Ces images sont d'un grand intérêt pour les …

Throughout stochastic data processing fields, mean and covariance matrices are commonly
employed for purposes such as standardizing multivariate data through decorrelation. For …