Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Michael J. Garee, PhD

Abstract

The original research goal was to combine the best techniques in the drone swarm literature and model a functional combat drone swarm that conducts a Suppression of Enemy Air Defense (SEAD) mission. However, the body of literature regarding Drone Swarm Target Assignment (DSTA) does not model enemy counteraction and assumes that the drones’ targets are compliant against destruction. Therefore, a model of enemy counteraction against drone swarms is developed, and Novel DSTA (NDSTA) is proposed to respond to the weaknesses of the current DSTA. Both methods of target assignment are combined with a tunable trajectory generation model, and the performance of DSTA vs. NDSTA is compared in an agent-based combat simulation. DSTA vs. NDSTA performance is compared using both a compliant enemy that cannot defend itself and a defiant enemy that can defend itself. Results show that NDSTA statistically outperforms DSTA against both a compliant and defiant enemy. Lastly, behavioral insights are obtained using a genetic algorithm (GA) to tune the model. These insights suggest the utility of using GA in future drone swarm research.

AFIT Designator

AFIT-ENS-MS-23-M-131

Comments

A 12-month embargo was observed.

Approved for public release. Case number on file.

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