Multi-Radar Tracking Optimization for Collaborative Combat

Despite great interest in recent research, in particular in China [1, 2] micromanagement of sensors by centralized command and control drives possible inefficiencies and risk into operations. Tactical decision making and execution by headquarters usually fail to achieve the speed necessary to meet rapid changes. Collaborative radars with C2 must provide decision superiority despite the attempts of an adversary to disrupt OODA cycles at all level of operations. Artificial intelligence can make a contribution for the purposes of coordinated conduct of the action, by improving the response time to threats and optimizing the allocation and the distribution of tasks within elementary smart radars. In order to address this problem, Thales and the private research lab NukkAI have been collaborating to introduce novel approaches for netted radars. Thales provided the simulation modeling the multi-radar target allocation problem and NukkAI proposed two novel reward-based learning approaches for the problem. In this paper, we present these two approaches: Evolutionary Single-Target Ordering (ESTO), which is based on evolution strategies and an RL approach based on Actor-Critic methods. To make the RL method tractable in practice, we introduce a simplification of the problem that we prove to be equivalent to solving the initial formulation. We evaluate our solutions on diverse scenarios of the aforementioned simulation.