PREDICTING FUTURE DESTINATIONS OF TACTICAL UNITS
Kim, Jun H.
Koyak, Robert A.
Bassett, Robert L.
MetadataShow full item record
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.
RightsThis 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.
Showing items related by title, author, creator and subject.
Johnson, Bonnie W. (Monterey, California: Naval Postgraduate SchoolMonterey, California. Naval Postgraduate School, 2019-12); NPS-19-N019-BNaval tactical operations could take a significant leap in progress with the aid of a real-time automated predictive analytics capability that provides predictions of second and third order effects of possible courses of ...
High bandwidth communications links between heterogeneous autonomous vehicles using sensor network modeling and extremum control approaches Kam, Khim Yee. (Monterey, California. Naval Postgraduate School, 2008-12);In future network-centric warfare environments, teams of autonomous vehicles will be deployed in a coorperative manner to conduct wide-area intelligence, surveillance and reconnaissance (ISR) missions in a tactical ...
Time-Critical Cooperative Path Following of Multiple Unmanned Aerial Vehicles over Time-Varying Networks Xargay, E.; Kaminer, I.I, Pascoal, A.M.; Hovakimyan, N.; Dobrokhodov, V.N.; Cichella, V.; Aguiar, P.; Ghabcheloo, R. (2013-03);This paper addresses the problem of steering a fleet of unmanned aerial vehicles along desired three-dimensional paths while meeting stringent spatial and temporal constraints. A representative example is the challenging ...