Deep Learning of UAS Electronic Warfare (EW) Tactics

Toyon Research Corporation proposes the research and development of Artificial Intelligence (AI) to optimize UAS control for non-kinetic effects to counter enemy Integrated Air Defense (IAD) systems.  We will focus on a single non-kinetic attack challenge problem; the generation of phantom tracks with small UAS against a modern networked set of threat radars. The problem requires coordination among the UAS team to cast a single coherent phantom track from the perspective of all adversary radar systems. To achieve this, the AI must optimize both the maneuvers of the UAS team and the controls of an onboard EW payload. Our selected approach combines deep reinforcement learning (DRL) with multi-agent training. The combination allows each agent to both learn and master complicated tasks from the experience gained while competing in a simulated environment. In Phase I, Toyon will design red-side IAD and blue-side UAS EW agent models. Toyon will also identify enhancements to its SLAMEM simulator to appropriately model the EW payload and missions of interest.  In Phase II, the agents and simulation enhancements will be implemented and the agents will be trained over millions of simulated mission sets using Toyon’s Modular Autonomy Incubator (MAUI) framework and high-performance computing cluster.