Relatore : Matthieu CORD

Co-relazione : SCHWANDER Olivier

Deep Learning and Information Geometry for Time-Series Classification

This work tackles the classification of structured time series, in particular micro-Doppler radar signals from non-cooperative drone targets. We first dwell upon the rich internal structure of such signals, which births a variety of different, yet relatable input representations which in turn may yield a variety of different learning models, namely convolutional and SPD neural networks. We then show how we can adapt known models to the structure of the data through the scope of information geometry, and go further by building novel schemes for the classification of such data. We finally demonstrate a full-classification pipeline making use of all aforementioned structures involved in data formation and classification, and present results thereupon on multiple datasets of real and synthetic data.

Pubblicazioni 2018-2020