The arrival on the market of a new generation of Imaging Radars 4D brings new opportunities and challenges for the development of data processing algorithms. These new sensors, geared towards the autonomous vehicle market, offer greater resolution thanks to a larger number of antennas. However, this implies an increase in the amount of data to be processed, which requires significant computing resources.
The aim of this thesis is to develop algorithms to optimize Radar resolution while limiting computational costs, in order to embed processing as close as possible to the Radar. To achieve this, beamforming techniques will be used to control the shape and direction of the Radar beam, so as to concentrate the energy in regions deemed relevant. One of the challenges is therefore to create a high-performance feedback loop to control the Radar antennas according to the scene observed during previous measurements.
This thesis will take an experimental approach, using a radar owned by the laboratory. Simulation tools will also be used to test hypotheses and go beyond the possibilities offered by the equipment.