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J. KIMS Technol > Volume 22(4); 2019 > Article
Journal of the Korea Institute of Military Science and Technology 2019;22(4):555-566.
DOI: https://doi.org/10.9766/KIMST.2019.22.4.555   
A Study on Methodology for Air Target Dynamic Targeting Applying Machine Learning
Junghyun Kang, Dongsoon Yim, Bongwan Choi
Department of Industrial Engineering, Hannam University
기계학습을 활용한 항공표적 긴급표적처리 발전방안 연구
강정현, 임동순, 최봉완
한남대학교 산업공학과
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.
Key Words: Machine Learning, Dynamic Targeting, Target Priority, Decision Tree


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