Publication:
PREDICTING FUTURE DESTINATIONS OF TACTICAL UNITS

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Authors
Kim, Jun H.
Subjects
Modeling Virtual Environments and Simulation
MOVES
random forest
neural networks
machine learning
supervised learning
tactical vehicle
battery
Advisors
Koyak, Robert A.
Date of Issue
2023-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The purpose of this thesis is to apply machine learning techniques towards predicting the future destinations of tactical units that move in a known road network. These units are modeled after standard field artillery batteries. Each battery is made up of eleven vehicles: four launcher vehicles, four reloading vehicles, two support vehicles, and one command control vehicle. Data was generated by the Modeling Virtual Environments and Simulation (MOVES) institute at NPS. There are two study questions: Can machine learning models accurately predict the future destinations of tactical vehicles? What is an adequate level of prediction accuracy for use in tactical applications?Of the current machine learning techniques, we use random forests and neural networks for destination prediction. Overall, our random forest achieves 38.9 percent prediction accuracy while our neural network achieves 43.2 percent prediction accuracy. There are four immediate directions for future research following this thesis. They are further investigation of prediction modeling, using data with measurement error collected on irregular time intervals, modeling with real world data, and multi-domain modeling.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
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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.
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