BROOKS Daniel

PhD student
Team : MLIA
Arrival date : 04/03/2017
Localisation : Campus Pierre et Marie Curie

Sorbonne Université - LIP6

Tel: +33 1 44 27 51 29, Daniel.Brooks (at) nulllip6.fr

Supervision : Matthieu CORD

Deep learning for radar signals

This PhD aims at developing new deep framework for radar signals. In this context, covariance matrices have attracted attention for machine learning applications due to their capacity to model meaningful statistical properties of the data. The main challenge is that one needs to take into account the particular geometry of the Riemaniann manifold of symmetric positive definite (SPD) matrices they belong to. In the context of neural networks, we propose in this PhD to study new deep architectures for handling these SPD matrices. We plan to introduce new projection layers based on the Riemaniann barycenter of the data, or other kinds of projections that could be learned.

2018-2019 Publications

Conference papers

2019
- D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Complex-valued neural networks for fully-temporal micro-Doppler classification”, 2019 20^th International Radar Symposium (IRS), 2019 20^th International Radar Symposium (IRS), Ulm, Germany, (IEEE) (2019)
- D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data”, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, pp. 3672-3676, (IEEE) (2019)
- D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Second-order networks in PyTorch”, GSI 2019 - 4^th International Conference on Geometric Science of Information, vol. 11712, Lecture Notes in Computer Science, Toulouse, France, pp. 751-758, (Springer) (2019)
2018
- D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Temporal Deep Learning for Drone Micro-Doppler Classification”, IRS 2018 - 19^th International Radar Symposium, Bonn, Germany, (IEEE) (2018)