AI-Augmented Multi Function Radar Engineering with Digital Twin: Towards Proactivity
| Autor: | Thomas Carpentier, Frédéric Barbaresco, Mathieu Klein, Florence Aligne, Rami Kassab, Yann Semet, Eric Jeanclaude, Yann Briheche, Nico de Bruijn, Konstantinos Varelas |
|---|---|
| Přispěvatelé: | Thales LAS France, Randomized Optimisation (RANDOPT ), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Thales Research and Technology [Palaiseau], THALES, THALES [France] |
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer science media_common.quotation_subject Real-time computing 02 engineering and technology law.invention [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Computer Science - Robotics Digital Twin 0203 mechanical engineering [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing law Artificial Intelligence 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Waveform Leverage (statistics) Electrical Engineering and Systems Science - Signal Processing Radar Function (engineering) media_common 020301 aerospace & aeronautics Multi-Mission Radar Black-Box Optimization Mode (statistics) 020206 networking & telecommunications Proactivity Proactive Radar [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Mixed-Integer Programming Augmented Engineering Robotics (cs.RO) |
| Zdroj: | 2020 IEEE International Radar Conference 2020 IEEE International Radar Conference, Sep 2020, Florence, Italy |
| DOI: | 10.48550/arxiv.2006.12384 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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