Surface-to-Air Missiles (SAMs) are crucial in modern air defense systems. A critical aspect of their effectiveness is the Engagement Zone (EZ), the spatial region within which a SAM can effectively engage and neutralize a target. Notably, the EZ is intrinsically related to the missile's maximum range; it defines the furthest distance at which a missile can intercept a target. The accurate computation of this EZ is essential but challenging due to the dynamic and complex factors involved, which often lead to high computational costs and extended processing times when using conventional simulation methods. In light of these challenges, our study investigates the potential of machine learning techniques, proposing an approach that integrates machine learning with a custom-designed simulation tool to train supervised algorithms. We leverage a comprehensive dataset of pre-computed SAM EZ simulations, enabling our model to accurately predict the SAM EZ for new input parameters. It accelerates SAM EZ simulations, enhances air defense strategic planning, and provides real-time insights, improving SAM system performance. The study also includes a comparative analysis of machine learning algorithms, illuminating their capabilities and performance metrics and suggesting areas for future research, highlighting the transformative potential of machine learning in SAM EZ simulations.

翻译：地空导弹在现代防空系统中至关重要，其效能的关键在于杀伤区——即导弹能够有效拦截并摧毁目标的空间区域。值得注意的是，杀伤区与导弹的最大射程密切相关，它定义了导弹拦截目标的最远距离。精确计算杀伤区至关重要，但由于涉及动态且复杂的因素，采用传统仿真方法往往导致计算成本高、处理时间长。针对这些挑战，本研究探索了机器学习技术的潜力，提出了一种将机器学习与定制仿真工具相结合的方法，用于训练监督学习算法。我们利用预先计算的地空导弹杀伤区仿真综合数据集，使模型能够针对新的输入参数准确预测杀伤区。这加速了地空导弹杀伤区仿真，增强了防空战略规划，并提供实时洞察，从而提升了地空导弹系统的性能。本研究还包含对机器学习算法的比较分析，揭示了它们的能力与性能指标，并指出了未来研究方向，凸显了机器学习在地空导弹杀伤区仿真中的变革潜力。