An Optimized Double-Nested Anti-Missile Force Deployment Based on the Deep Kuhn–Munkres Algorithm

Wen Sun, Zeyang Cao, Gang Wang, Yafei Song () and Xiangke Guo
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Wen Sun: Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China
Zeyang Cao: Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China
Gang Wang: Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China
Yafei Song: Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China
Xiangke Guo: Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China

Mathematics, 2022, vol. 10, issue 23, 1-17

Abstract: In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment.

Keywords: anti-missile; force deployment; double nested optimization; deep Kuhn–Munkres algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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