RED CELL ANALYSIS FOR MOBILE NETWORKED CONTROL SYSTEMS
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Authors
Wigington, Larry W.
Subjects
UAV
machine learning
adversarial machine learning
AI vs. AI
NCS
control systems
counter-reconnaissance
expeditionary advanced base operations
multi-domain operations
machine learning
adversarial machine learning
AI vs. AI
NCS
control systems
counter-reconnaissance
expeditionary advanced base operations
multi-domain operations
Advisors
Horner, Douglas P.
Yoshida, Ruriko
Date of Issue
2021-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
In the near future, networked unmanned autonomous systems will increasingly be employed to support ground force operations. Approaches to collaborative control can find near-optimal position recommendations that optimize over system parameters such as sensing and communication to increase mission effectiveness. However, over time these recommendations can create predictable paths that may provide leading indications of the force’s operational intent. Using time series forecasting methods and deep neural networks, this thesis conducts an adversarial assessment of unmanned mobile networked control systems. In the first scenario, the path of the team’s ground motion predicted by the model follows the initially planned but not executed path. In a second scenario, the model achieves a maximum path error rate of only 75 meters. In both cases, this methodology correctly identifies the direction and distance the team would travel and even identified points where the team changed direction, allowing the autonomous red cell analysis to discern the ground force’s intent. These results indicate that automated red cell analysis is a potentially valuable component in planning and executing unmanned mobile networked control systems supporting expeditionary ground teams. It provides near real-time feedback on the unmanned agents’ paths to determine if course adjustments can reduce operational intent predictability.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
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NPS Report Number
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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.