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

Classification of radar observations with machine learning tools is of primary importance for
the identification of non-cooperative radar targets such as drones. These observations are …

This paper focuses on the study of open curves in a Riemannian manifold M, and proposes
a reparametrization invariant metric on the space of such paths. We use the square root …

The aim of this paper is to find an optimal matching between manifold-valued curves, and
thereby adequately compare their shapes, seen as equivalent classes with respect to the …

The rich information in the power spectral density (PSD) matrix of a received signal can be
extracted in different ways for various purposes in signal processing. Care, however, must …

In its raw form, micro-Doppler radar data takes the form of a complex time-series, which can
be seen as multiple realizations of a Gaussian process. As such, a complex covariance …

In this work, we build dedicated learning models for micro-Doppler radar time series
classification. We develop both deep temporal architectures based on time-frequency …

Machine Learning, and in particular Deep Learning, is a powerful tool to model and study
the intrinsic statistical foundations of data, allowing the extraction of meaningful, human …