小样本多批次增长试验下弹药散布参数的贝叶斯估计方法

胡岑语; 史宪铭; 王亚彬; 常超; 姜钰; 龚影广

doi:10.12382/bgxb.2026.0384

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小样本多批次增长试验下弹药散布参数的贝叶斯估计方法

更新时间：2026-09-08

- 小样本多批次增长试验下弹药散布参数的贝叶斯估计方法
- Bayesian Estimation of Ammunition Dispersion Parameters Under Small-Sample Multi-Batch Growth Tests
- 兵工学报 2026年
- 作者机构：
  
  陆军工程大学石家庄校区, 河北石家庄050004
- 作者简介：
  
  胡岑语（2001-），男，硕士研究生，主要研究方向为系统工程、消耗预测、供应管理。 E-mail：1339816023@qq.com
  邮箱：sxm@nudt.edu.cn
  王亚彬（1975-），男，教授，博士后，主要研究方向为装备保障，供应管理。 E-mail：sxm@nudt.edu.cn
  常超（1989-），男，硕士研究生，主要研究方向为系统工程、后勤保障。 E-mail：1339816023@qq.com
  姜钰（2003-），男，硕士研究生，主要研究方向为系统工程、消耗预测、供应管理。 E-mail：2909648538@qq.com
  龚影广（1989-），男，硕士研究生，主要研究方向为系统工程、后勤保障。 E-mail：gyg198902@qq.com
- 基金信息：
- DOI：10.12382/bgxb.2026.0384
  中图分类号： TJ301
- 收稿：2026-04-27，
  
  修回：2026-09-03，
  
  录用：2026-06-10，
- 稿件说明：
移动端阅览
胡岑语, 史宪铭, 王亚彬, 等. 小样本多批次增长试验下弹药散布参数的贝叶斯估计方法[J/OL]. 兵工学报, 2026.

Hu Cenyu, Shi Xianming, Wang Yabin, et al. Bayesian Estimation of Ammunition Dispersion Parameters Under Small-Sample Multi-Batch Growth Tests[J/OL]. Acta Armamentarii, 2026.
胡岑语, 史宪铭, 王亚彬, 等. 小样本多批次增长试验下弹药散布参数的贝叶斯估计方法[J/OL]. 兵工学报, 2026. DOI： 10.12382/bgxb.2026.0384.

Hu Cenyu, Shi Xianming, Wang Yabin, et al. Bayesian Estimation of Ammunition Dispersion Parameters Under Small-Sample Multi-Batch Growth Tests[J/OL]. Acta Armamentarii, 2026. DOI： 10.12382/bgxb.2026.0384.

摘要

针对弹药多批次迭代研发过程中小样本试验数据稀缺、历史信息利用不充分导致评估精度低的难题，提出一种融合多批次增长特性的贝叶斯评估方法。该方法基于弹着点二维正态散布规律构建多等级毁伤概率模型，引入弹药性能单调递增顺序约束，采用正态-逆伽马共轭先验推导多批次参数联合后验分布，设计带约束的Gibbs抽样算法实现多批次散布参数联合估计。示例分析表明，在每批次仅10发条件下，各批次参数估计均满足顺序约束，核心散布参数绝对误差最低为0.0080m。第3批轻度及以上累积毁伤概率达96.62%，各等级毁伤概率随批次递增，与性能提升规律一致，可为弹药研发迭代与作战毁伤概率评估提供可靠支撑。

Abstract

To address the challenge of low evaluation accuracy caused by scarce small-sample test data and insufficient utilization of historical information in the multi-batch iterative development of ammunition

a Bayesian evaluation method integrating multi-batch growth characteristics is proposed. Based on the two-dimensional normal dispersion law of impact points

a multi-level damage probability model is constructed

a monotonic increasing order constraint on ammunition performance is introduced

a normal-inverse gamma conjugate prior is adopted to derive the joint posterior distribution of multi-batch parameters

and a constrained Gibbs sampling algorithm is designed to realize the joint estimation of multi-batch dispersion parameters. Numerical examples show that

under the condition of only 10 rounds per batch

the parameter estimates of all batches satisfy the order constraints

with a minimum absolute error of 0.0080 m for the core dispersion parameters; the cumulative damage probability of mild or above for the third batch reaches 96.62%

and the damage probabilities at all levels increase monotonically with the batch number

consistent with the physical law of performance improvement. The proposed method can provide reliable support for the iterative development of ammunition and combat damage probability evaluation.

关键词

Keywords

references

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