Multi-Radar Tracking Optimization for Collaborative Combat

• URL: http://arxiv.org/abs/2010.11733v1
• Date: Tue, 20 Oct 2020 07:42:58 GMT
• Title: Multi-Radar Tracking Optimization for Collaborative Combat
• Authors: Nouredine Nour, Reda Belhaj-Soullami, C\'edric Buron, Alain Peres, Fr\'ed\'eric Barbaresco
• Abstract summary: We propose two novel reward-based learning approaches to decentralized netted radar coordination. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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