We develop a new geometric approach for high resolution Doppler processing based on the
Riemannian geometry of Toeplitz covariance matrices and the notion of Riemannian p …

Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

Abstract Information Geometry has been introduced by Rao, and axiomatized by Chentsov,
to define a distance between statistical distributions that is invariant to non-singular …

An affective brain-computer interface (aBCI) has demonstrated great potential in the field of
emotion recognition. However, existing aBCI models encounter significant challenges in …

Illegal, malicious or dangerous uses of drones, require developing systems capable of
detecting, tracking and recognizing them in a non-collaborative way, and with enough …

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 …

Polarimetric SAR images have a large number of applications. To extract a physical
interpretation of such images, a classification on their polarimetric properties can be a real …

The estimation of covariance matrix is of fundamental importance in radar signal processing.
Recent work has shown that information geometry provides a novel approach to estimating …

In a complex electromagnetic environment, there are cases where the noise is uncertain and
difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper …

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 …