A geometric approach to covariance matrix estimation and its applications to radar problems
A Aubry, A De Maio, L Pallotta - IEEE Transactions on Signal …, 2017 - ieeexplore.ieee.org
A new class of disturbance covariance matrix estimators for radar signal processing
applications is introduced following a geometric paradigm. Each estimator is associated with …
applications is introduced following a geometric paradigm. Each estimator is associated with …
Exploring complex time-series representations for Riemannian machine learning of radar data
DA Brooks, O Schwander, F Barbaresco… - ICASSP 2019-2019 …, 2019 - ieeexplore.ieee.org
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 …
the identification of non-cooperative radar targets such as drones. These observations are …
Computing distances and geodesics between manifold-valued curves in the SRV framework
AL Brigant - arXiv preprint arXiv:1601.02358, 2016 - arxiv.org
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 …
a reparametrization invariant metric on the space of such paths. We use the square root …
A discrete framework to find the optimal matching between manifold-valued curves
A Le Brigant - Journal of Mathematical Imaging and Vision, 2019 - Springer
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 …
thereby adequately compare their shapes, seen as equivalent classes with respect to the …
Mean and median of PSD matrices on a Riemannian manifold: Application to detection of narrow-band sonar signals
KM Wong, JK Zhang, J Liang… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
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 …
extracted in different ways for various purposes in signal processing. Care, however, must …
A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing
D Brooks, O Schwander, F Barbaresco… - 2019 International …, 2019 - ieeexplore.ieee.org
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 …
be seen as multiple realizations of a Gaussian process. As such, a complex covariance …
Deep learning and information geometry for drone micro-Doppler radar classification
D Brooks, O Schwander, F Barbaresco… - 2020 IEEE Radar …, 2020 - ieeexplore.ieee.org
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 …
classification. We develop both deep temporal architectures based on time-frequency …
Deep Learning and Information Geometry for Time-Series Classification
D Brooks - 2020 - theses.hal.science
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 …
the intrinsic statistical foundations of data, allowing the extraction of meaningful, human …