arXiv cs.AI· Joao P. A. Dantas, Andre N. Costa, Diego Geraldo, Marcos R. O. A. Maximo, Takashi Yoneyama·· 12 小时前AI 评分20
PoKER：基于机器学习与随机目标的空空导弹杀伤概率估计率模型
PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets
AI 导读
PoKER 是一种面向超视距空战的概率模型，用机器学习预测导弹杀伤概率，替代传统按最大、最小和不可逃逸距离划分的静态 WEZ 模型。该模型纳入目标的随机行为与导弹脱靶距离，并结合战斗部杀伤力、目标与射手朝向及具体交战条件量化成功概率。研究已发表于 The Journal of Defense Modeling and Simulation，代码已公开。
正文 · 原文
Abstract: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.
| Comments: | Accepted manuscript. 18 pages, 10 figures, 5 tables. Code available at this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.6.5 |
| Cite as: | arXiv:2610.32864 [cs.LG] |
| (or arXiv:2610.32864v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32864 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology (2025) |
| Related DOI: | https://doi.org/10.1177/15485129241309675
DOI(s) linking to related resources |
Submission history
From: Joao P. A. Dantas [view email]
[v1]
Fri, 23 Oct 2026 16:34:57 UTC (5,413 KB)
来源：arXiv cs.AI · arxiv.org