Radar technologies have gained significant interest in recent years, particularly with the emergence of MIMO radars and "Imaging Radars 4D". This new generation of radar offers both opportunities and challenges for the development of perception algorithms. Traditional algorithms such as FFT, CFAR, and DOA are effective for detecting moving targets, but the generated point clouds are still too sparse for precise environment model. This is a critical issue for autonomous vehicles and robotics.

This thesis proposes to explore unsupervised Machine Learning techniques to improve environment model from radar data. The objective is to produce a richer model of the environment to enhance data density and scene description, while controlling computational costs for real-time computing. The thesis will address the question of which types of radar data are best suited as inputs for algorithms and for representing the environment. The candidate will need to explore non-supervised algorithmic solutions and seek computational optimizations to make these solutions compatible with real-time execution.

Ultimately, these solutions must be designed to be embedded as close as possible to the sensor, allowing them to be executed on constrained targets.