系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (2): 615-626.doi: 10.12305/j.issn.1001-506X.2026.02.22

• 系统工程 • 上一篇    

基于Gamma分布的装甲车辆目标弹药消耗贝叶斯推断

王亚龙1, 史宪铭1,*, 赵乾1, 刘昊邦2, 梁俊鹏1   

  1. 1. 陆军工程大学石家庄校区,河北 石家庄 050003
    2. 海军工程大学管理工程与装备经济系,湖北 武汉 430033
  • 收稿日期:2024-08-27 修回日期:2025-04-10 出版日期:2025-05-20 发布日期:2025-05-20
  • 通讯作者: 史宪铭
  • 作者简介:王亚龙(1989—),男,硕士研究生,主要研究方向为系统工程、消耗预测
    赵 乾(1986—),男,讲师,博士,主要研究方向为管理科学与工程
    刘昊邦(1998—),男,博士研究生,主要研究方向为系统工程、装备管理与保障
    梁俊鹏(1994—),男,硕士研究生,主要研究方向为装备供应保障理论
  • 基金资助:
    省级科研基金项目;省级研究生资助课题

Bayesian inference of ammunition consumption for armored vehicle targets based on Gamma distribution

Yalong WANG1, Xianming SHI1,*, Qian ZHAO1, Haobang LIU2, Junpeng LIANG1   

  1. 1. Shijiazhuang Campus of Army Engineering University,Hebei 050003,China
    2. Department of Management Engineering and Equipment Economics,Naval University of Engineering,Wuhan 430033,China
  • Received:2024-08-27 Revised:2025-04-10 Online:2025-05-20 Published:2025-05-20
  • Contact: Xianming SHI

摘要:

针对装甲车辆目标毁伤评估中弹药消耗量预测存在的小样本建模难题,提出递进式四区毁伤动态建模方法,通过解构目标结构树,建立递进毁伤机制,结合毁伤面积比与效能权重,创新设计效能向下包含的动态累积规则,突破传统静态毁伤阈值局限;揭示目标效能毁伤Gamma分布规律,构建融合区域毁伤概率的双向参数修正模型,实现弹药消耗量分布形态的精准刻画;研发贝叶斯-马尔可夫链-蒙特卡罗混合求解算法,提出先验信息一致性检验转换定理,设计Jeffreys-仿真联合先验分布与自适应Metropolis-Hastings采样策略,大幅降低参数估计误差。实例验证表明,模型对不同毁伤等级下的弹药消耗预测误差均控制在5%以内,为小样本毁伤评估提供了具备战场适应性的新型分析框架。

关键词: 弹药消耗, 效能毁伤, Gamma分布, 装甲车辆, 贝叶斯推断, 马尔可夫链-蒙特卡罗

Abstract:

Aiming at the critical challenge of small-sample modeling in ammunition consumption prediction for armored vehicle target damage assessment, progressive four-zone damage dynamic modeling is proposed. A hierarchical damage propagation mechanism is established through deconstruction of target structural trees. By integrating damage area ratios with efficacy weighting coefficients, this work pioneers dynamic cumulative rules with downward-inclusive efficacy, effectively overcoming limitations of traditional static damage threshold approaches. Gamma distribution characterization of target efficacy degradation is revealed. A bidirectional parameter correction model is developed by incorporating zonal damage probabilities, enabling precise characterization of ammunition consumption distribution patterns. Bayesian-Markov chain-Monte Carlo hybrid solving algorithm is developed. A prior information consistency verification conversion theorem is proposed, coupled with Jeffreys-simulation joint prior distribution construction and adaptive Metropolis-Hastings sampling strategies, achieving significant reduction in parameter estimation errors. Case validation demonstrates that the proposed framework maintains prediction errors below 5% across multiple damage severity levels, establishing a battlefield-adaptive analytical paradigm for small-sample damage assessment scenarios. This methodology advances conventional approaches through its integration of structural dynamics, statistical pattern recognition, and adaptive Bayesian computation, providing a robust theoretical foundation and practical toolkit for ammunition logistics optimization in modern warfare.

Key words: ammunition consumption, effectiveness destruction, Gamma distribution, armored vehicle, Bayesian inference, Markov chain-Monte Carlo

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