AI-Augmented Multi Function Radar Engineering with Digital Twin: Towards Proactivity
This work addresses the need for efficient engineering tools in radar systems to enhance operational capabilities, though it appears incremental in applying existing AI methods to a specific domain.
The paper tackled the challenge of designing operational modes for new-generation digital radars by developing an AI-augmented engineering tool using optimization algorithms and a radar Digital Twin, which achieved high computation speeds enabling dynamic mode proposals.
Thales new generation digital multi-missions radars, fully-digital and software-defined, like the Sea Fire and Ground Fire radars, benefit from a considerable increase of accessible degrees of freedoms to optimally design their operational modes. To effectively leverage these design choices and turn them into operational capabilities, it is necessary to develop new engineering tools, using artificial intelligence. Innovative optimization algorithms in the discrete and continuous domains, coupled with a radar Digital Twins, allowed construction of a generic tool for "search" mode design (beam synthesis, waveform and volume grid) compliant with the available radar time budget. The high computation speeds of these algorithms suggest tool application in a "Proactive Radar" configuration, which would dynamically propose to the operator, operational modes better adapted to environment, threats and the equipment failure conditions.