PoKER：基于机器学习的空空导弹杀伤概率估算模型
PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets
一篇军用方向的论文：作者用机器学习取代传统的静态 WEZ 距离分区，预测超视距空战中导弹的杀伤概率，连目标随机机动都算进去了。
arXiv 论文提出 PoKER，一种用于超视距（BVR）空战的杀伤概率估算模型。传统 Weapon Engagement Zone（WEZ）模型只按最大、最小和不可逃逸距离划分静态区域，PoKER 则用机器学习预测杀伤概率，并纳入目标的随机行为与导弹脱靶距离。该模型还考虑战斗部杀伤力、目标与发射机朝向、具体交战条件等因素，量化交战成功率。论文称其可用于辅助 BVR 空战飞行员的作战决策。
PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets
This work introduces PoKER, a novel probabilistic model engineered to optimize missile launch effectiveness in air-to-air scenarios, specifically within Beyond Visual Range (BVR) air combat. Unlike conventional Weapon Engagement Zone (WEZ) models that delineate zones based on static distances such as maximum, minimum, and no-escape ranges, PoKER applies machine learning to predict kill probabilities more accurately by integrating the stochastic behaviors of targets and missile miss distances. This model dynamically evaluates target behavior, greatly expanding the predictive capabilities of engagement analysis. By factoring in elements such as warhead lethality, target and shooter orientations, and the specific conditions of engagement, PoKER provides important insights into engagement dynamics and quantifies success probabilities. Consequently, it can potentially be an important tool for BVR air combat pilots, improving operational decision-making within this specialized combat domain.