基于特小子样下弹药命中概率的贝叶斯改进算法

doi:10.12305/j.issn.1001-506X.2026.04.20

Abstract

Abstract:

Addressing the issue of low accuracy in calculating the hit probability of high-performance ammunition in ultra-small subsample using traditional methods, an improved algorithm based on Bayesian hypothesis testing is proposed. Under the assumption that the distribution of ammunition impact points follows a circular dispersion, the calculation formula for the hit probability is derived by analyzing the marginal distribution of the ammunition miss distance. By utilizing Bayesian hypothesis testing, the cumulative distribution function of the hypothesis testing parameters is calculated using both indirect and direct algorithms while fully utilizing prior information. The ammunition impact point density is estimated under the premise of satisfying the confidence interval required by the specified testing level. Simulation results show that the proposed algorithm significantly simplifies the calculation steps and improves the accuracy of the calculation results compared to traditional methods, providing a reference for calculating the hit probability of ammunition under ultra-small subsample.

Key words: ultra-small subsample, Bayesian inference, ammunition hit probability, dispersion density, hypothesis testing

CLC Number:

TJ 413

Yalong WANG, Xianming SHI, Zhao LIU, Haobang LIU, Wei XIA. Bayesian improved algorithm for estimating ammunition hit probability under ultra-small subsample[J]. Systems Engineering and Electronics, 2026, 48(4): 1322-1330.

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序号	脱靶量r_i/m
Y₁	5.732
Y₂	4.683
Y₃	5.421

序号	脱靶量r_i/m
X₁₁	4.589
X₁₂	4.432
X₁₃	5.514
X₁₄	6.023
X₁₅	5.061

组别	$ {\sigma }_{i}{}^{2} $	组别	$ {\sigma }_{i}{}^{2} $
X₁	0.341	X₁₁	0.827
X₂	0.878	X₁₂	0.286
X₃	0.539	X₁₃	0.730
X₄	1.259	X₁₄	1.319
X₅	0.580	X₁₅	0.528
X₆	0.538	X₁₆	1.062
X₇	0.613	X₁₇	0.569
X₈	0.891	X₁₈	2.645
X₉	0.354	X₁₉	0.639
X₁₀	6.280	X₂₀	1.421

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Bayesian improved algorithm for estimating ammunition hit probability under ultra-small subsample

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