Machine Learning (ML) in aerospace and defense (A&D) industries: a systematic literature review

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Authors
Khan, Lina
Elshennawy, Ahmad
Furterer, Sandy
Cudney, Elizabeth A.
Advisors
Issue Date
2024-10
Type
Article
Keywords
Machine learning , Learning algorithms , Military , Defense industries
Research Projects
Organizational Units
Journal Issue
Citation
Khan, L., Elshennawy, A., Furterer, S., Cudney, E. (2024). Machine Learning (ML) in aerospace and defense (A&D) industries: a systematic literature review. Journal of Management & Engineering Integration, 17(2), 66-82. https://doi.org/10.62704/10057/28466
Abstract

In recent years, Machine Learning (ML) has made significant technical progress due to increasedavailability of larger data sets, more powerful computing performance, and greater budget allocations. However, ethical, political, and societal implications relate to successful and global implementation of ML in Aerospace and Defense (A&D) environments. Such challenges could result in unpredictability or inexplicability of the ML-enabled operation. This study provides a systematic review of published material on ML methods, limitations, and current and potential applications of ML in A&D. Records from 2012 to 2021 were found using multiple databases and showed ample research in missions changing from manned to semi-autonomous or autonomous; specifically in areas related to surveillance, reconnaissance, logistics, intelligence, and command and control. The results also emphasize the implementation of ML is deemed both threatening and promising.

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Description
Published in SOAR: Shocker Open Access Repository by the Wichita State University Libraries Technical Services, October 2024.
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Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.17 no.2
PubMed ID
ISSN
1939-7984
EISSN