Intercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignment

Date
2020
Authors
Montalbano, Nicholas G.
Humphreys, Todd E.
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract

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.

Description
Citation
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.