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

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