SIMULATED LASER WEAPON SYSTEM DECISION SUPPORT TO COMBAT DRONE SWARMS WITH MACHINE LEARNING
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Authors
Edwards, Daniel M.
Subjects
laser weapon system
machine learning
unmanned aerial vehicles
unmanned aircraft systems
modeling and simulation
Modeling Virtual Environments and Simulation
MOVES
machine learning
unmanned aerial vehicles
unmanned aircraft systems
modeling and simulation
Modeling Virtual Environments and Simulation
MOVES
Advisors
Johnson, Bonnie W.
Date of Issue
2021-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This thesis demonstrates an application of machine learning for enabling automated decision support to warfighters operating laser weapon systems in complex tactical situations. The thesis used the NPS Modeling Virtual Environments and Simulation (MOVES) Institute's Swarm Commander modeling and simulation software environment to develop simulated datasets of wargaming scenarios involving a shipboard laser weapon system defending against drone swarm threats. The simulated datasets were used to train a machine learning algorithm to predict the optimum engagement strategy in a complex battlespace with heterogeneous drone swarms. Multiple machine learning techniques were evaluated, and the classification tree technique was selected as the preferred approach. The final algorithm had an overall accuracy of 96% in correctly predicting engagement outcomes based on drone threat types, quantities, and the laser weapon system attack strategy. The research results demonstrate (1) the utility of modeling and simulation for supporting the development of tactical machine learning applications, (2) the potential for machine learning to support future tactical operations, and (3) the potential for machine learning and automation, in general, to reduce the cognitive load on future warfighters faced with making critical decisions in complex threat environments.
Type
Thesis
Description
Series/Report No
NPS Outstanding Theses and Dissertations
Department
Systems Engineering (SE)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.