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http://dx.doi.org/10.9766/KIMST.2019.22.4.555

A Study on Methodology for Air Target Dynamic Targeting Applying Machine Learning  

Kang, Junghyun (Department of Industrial Engineering, Hannam University)
Yim, Dongsoon (Department of Industrial Engineering, Hannam University)
Choi, Bongwan (Department of Industrial Engineering, Hannam University)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.22, no.4, 2019 , pp. 555-566 More about this Journal
Abstract
In order to prepare for the future warfare environment, which requires a faster operational tempo, it is necessary to utilize the fourth industrial revolution technology in the field of military operations. This study propose a methodology, 'machine learning based dynamic targeting', which can contribute to reduce required man-hour for dynamic targeting. Specifically, a decision tree algorithm is considered to apply to dynamic targeting process. The algorithm learns target prioritization patterns from JIPTL(Joint Integrated Prioritized Target List) which is the result of the deliberate targeting, and then learned algorithm rapidly(almost real-time) determines priorities for new targets that occur during ATO(Air Tasking Order) execution. An experiment is performed with artificially generated data to demonstrate the applicability of the methodology.
Keywords
Machine Learning; Dynamic Targeting; Target Priority; Decision Tree;
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