A new class of disturbance covariance matrix estimators for radar signal processing
applications is introduced following a geometric paradigm. Each estimator is associated with …

This paper studies the generalization error of invariant classifiers. In particular, we consider
the common scenario where the classification task is invariant to certain transformations of …

Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is
considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) …

This paper addresses the problem of the clutter subspace projector estimation in the context
of a disturbance composed of a low rank heterogeneous (Compound Gaussian) clutter and …

We consider robust covariance estimation with an emphasis on Tyler's M-estimator. This
method provides accurate inference of an unknown covariance in non-standard settings …

In a seminal paper, Tyler suggests an M-estimator for shape, which is now known as Tyler's
shape matrix. Tyler's shape matrix is increasingly popular due to its nice statistical …

In this paper, an automatic classification approach for polarimetric covariance structure is
derived and assessed. It extends the framework of Pallotta et al.“Detecting Covariance …

We study the Gaussian and robust covariance estimation, assuming the true covariance
matrix to be a Kronecker product of two lower dimensional square matrices. In both settings …

To tackle the issue of space-time adaptive processing (STAP) performance degradation
caused by inaccurate estimation of the clutter covariance matrix (CCM) with limited sample …

In this paper, we consider polarimetric adaptive detection in compound Gaussian clutter
whose covariance matrix (CM) has a Kronecker structure. We derive the Cramér-Rao bound …