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Conference Papers Year : 2020

Multi-Radar Tracking Optimization for Collaborative Combat

Nouredine Nour
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Reda Belhaj-Soullami
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  • PersonId : 1079590
Alain Peres
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  • PersonId : 1079591
Frédéric Barbaresco

Abstract

Smart Grids of collaborative netted radars accelerate kill chains through more efficient cross-cueing over centralized command and control. In this paper, we propose two novel reward-based learning approaches to decentralized netted radar coordination based on black-box optimization and Reinforcement Learning (RL). To make the RL approach tractable, we use a simplification of the problem that we proved to be equivalent to the initial formulation. We apply these techniques on a simulation where radars can follow multiple targets at the same time and show they can learn implicit cooperation by comparing them to a greedy baseline.
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Dates and versions

hal-02971759 , version 1 (19-10-2020)

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Nouredine Nour, Reda Belhaj-Soullami, Cédric Buron, Alain Peres, Frédéric Barbaresco. Multi-Radar Tracking Optimization for Collaborative Combat. Conference On Artificial Intelligence in Defense (CAID'2020), Nov 2020, Rennes, France. ⟨hal-02971759⟩
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