Multi-Radar Tracking Optimization for Collaborative Combat
This addresses coordination challenges in military radar networks for faster target engagement, though it appears incremental as it builds on existing learning methods.
The paper tackles the problem of decentralized coordination in netted radar systems for collaborative combat, proposing two reward-based learning approaches (black-box optimization and RL) and demonstrating they can learn implicit cooperation in multi-target tracking simulations.
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.