This paper deals with radar clutter statistical learning based on spatial Doppler fluctuation. In
articles [1]-[4], data is clustered cell by cell. In this article, we generalize the previous model …

This paper deals with unsupervised radar clutter clustering to characterize pathological
clutter based on their Doppler fluctuations. Operationally, being able to recognize …

Here we propose a method to classify radar clutter from radar data using an unsupervised
classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz …

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

The Riemannian geometry of covariance matrices has been essential to several successful
applications, in computer vision, biomedical signal and image processing, and radar data …

In this letter, the problem of environment classification in the radar context is addressed.
Specifically, adaptive architectures are conceived to classify training data, used for …

In this paper, the problem of classifying radar clutter returns into statistically homogeneous
subsets is addressed. To this end, latent variables, which represent the classes to which the …

The performance of adaptive radar in range-heterogeneous clutter is a topic of practical
interest, since radars are often trained over nonhomogeneous stretches of clutter. In this …

This paper proposes innovative classification architectures capable of identifying whether or
not the secondary data contains a clutter edge and/or clutter discretes/outliers. To this end …

This paper focuses on the robustness aspects of Doppler signal processing tasks with
Machine Learning algorithms within the context of pathological radar clutter classification …