This paper examines the use of neural networks to perform low-level control calculations within a larger game-theoretic framework for drone swarm interception. As unmanned aerial vehicles (UAVs) become more capable and less expensive, their malicious use becomes a greater public threat. This paper examines the problem of intercepting rogue UAV swarms by exploiting the underlying game-theoretic nature of large-scale pursuit-evasion games to develop locally optimal profiles for target assignment. It paper also examines computationally efficient means to streamline this process.

Cite and download the paper:
Nicholas G. Montalbano and Todd E. Humphreys, "Intercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignment," In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 36-43. IEEE, 2020.