OPERATIONAL PLANNING AND OPTIMIZATION OF SMALL DOMAIN SWARM DEFENSE STRATEGIES
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Authors
Wish, Michael J.
Subjects
swarm
drone
optimization
strategy
travelling salesman problem
machine learning
defense
drone
optimization
strategy
travelling salesman problem
machine learning
defense
Advisors
Kang, Wei
Clark, Abram H., IV
Date of Issue
2022-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This thesis considers the case of a drone defending a high-value target from a number of inbound attacking drones. The defending drone is equipped with short-range weapons and must destroy each of the attacking drones in the most efficient manner. This problem sits at the intersection of several open problems in applied mathematics, such as optimal motion planning in the presence of attrition, as well as solving a “traveling salesman problem” (TSP) with moving targets. The purpose of our research was to analyze this problem by decomposing it into the component problems and then presenting proof-of-concept solutions of each component. The primary results of this thesis include a modeling framework where optimization can be performed without requiring constraints; comparing the strengths of using different types of cost functions for optimization (e.g., minimizing the chance of high-value unit destruction versus a metric based on the path of the defender relative to attackers); and solving moving-target TSP in certain limits by mapping it onto standard TSP or using machine learning.
Type
Thesis
Description
Series/Report No
Department
Physics (PH)
Applied Mathematics (MA)
Organization
Identifiers
NPS Report Number
Sponsors
Cruiser/ONR
Funding
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.