AI-Augmented Multi Function Radar Engineering with Digital Twin: Towards Proactivity

TL;DR

This paper presents an AI-driven engineering framework utilizing digital twins for multi-mission radar design, enabling proactive mode optimization to adapt dynamically to operational conditions.

Contribution

It introduces a novel AI-based optimization tool for radar mode design that leverages digital twins for real-time, adaptive operational capabilities.

Findings

High-speed algorithms enable dynamic mode optimization.

The tool supports proactive radar configurations.

Enhanced adaptability to environment and threat conditions.

Abstract

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.