Taking place at SUAD, this joint programme will develop advanced expertise in the field of hybrid AI applied to sensors and its use within the design and implementation of digital twins. 

 

It will first focus on the reconfiguration of digital antenna beam training in case of transmitter/receiver failures. This theme will tackle hybrid AI issues through the integration of physical models of antenna and machine-learning-based models using representative measurements of the coupling between transmitter/receiver modules, or between radar faces.

It will then deal with the design of search modes for collaborative proactive radars. In order to optimize the operational performance of detection, the objective will be to develop a tool for designing grids and waveforms for monitoring functions, based on radar’s digital twin. As an example, methods of Reinforcement Learning relying on the emulation of scenarios by the digital twin will be explored.