Assessment of deep learning for analyzing radar signals in maritime environment

The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge (e.g., detection of objects in noisy scenes from a maritime environment caused by the interference produced by the reflection of the radar waves on the sea). The project is broken down into three main sub-goals: (1) review of relevant literature on radar detection in a maritime environment, (2) exploitation of existing datasets (e.g., IPIX, CSIR), and the state-of-the-art on radar simulation, and (3) application of DL, in particular convolutional neural networks (CNN), for suppressing sea clutter and detecting targets in radar image in maritime environment.

Faculty Supervisor:

Christian Gagné

Student:

Mohamed Abid

Partner:

Thales Canada Inc.

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Université Laval