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

• Published in: Mathematics (Basel) Vol. 10; no. 23; p. 4627
• Main Authors: Sun, Wen, Cao, Zeyang, Wang, Gang, Song, Yafei, Guo, Xiangke
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Algorithms; Analysis; anti-missile; Antimissile missiles; Ballistic missiles; Battlefields; Cover zone; deep Kuhn–Munkres algorithm; Defense; Deployment (Strategy); double nested optimization; Dynamic programming; force deployment; Genetic algorithms; Interception; Machine learning; Mathematical optimization; Methods; Military air strikes; Military policy; Missiles; Optimization; Optimization algorithms; Problem solving; Queuing theory; War; Weapon systems; Weapons; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10234627

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.