Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

The qualified missile damage effectiveness is an important guarantee for giving full play to combat effectiveness. In order to test whether the missile damage effectiveness meets the standard, it is particularly necessary to carry out damage effectiveness identification test research. In view of the limitation that the existing "single-shot damage probability" damage effectiveness quantitative characterization index is difficult to fully describe the missile damage effectiveness based on a single damage standard, considering the different damage level results formed by a single missile hitting the target, the damage effectiveness is characterized by the probability of occurrence of different damage level results. Missile identification from the perspective of multiple damage level standards is conducive to comprehensive inspection of missile damage effectiveness. In addition, in order to overcome the problem of insufficient information in small sample missile assembly tests, the Bayesian method is combined with the system contribution degree to fuse multi-source prior information in the identification test design, and a test scheme with full information utilization and controllable risks for both parties is designed. The example shows that compared with the quantitative characterization index of a single damage standard, this study can fully describe the missile damage effectiveness, and compared with the improved binomial distribution hypothesis test method, the superiority of this method is verified.

Authors

Publication Details

Journal
Journal of applied artificial intelligence.
Published
2024-10-18
DOI
https://doi.org/10.59782/aai.v1i2.292
Primary Topic
Advanced Measurement and Detection Methods
Type
article
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article

Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

Haobang Liu, Tao Hu, Tong Chen
Journal of applied artificial intelligence.
Advanced Measurement and Detection Methods
article

Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

Haobang Liu, Tao Hu, Tong Chen
article en

Abstract

The qualified missile damage effectiveness is an important guarantee for giving full play to combat effectiveness. In order to test whether the missile damage effectiveness meets the standard, it is particularly necessary to carry out damage effectiveness identification test research. In view of the limitation that the existing "single-shot damage probability" damage effectiveness quantitative characterization index is difficult to fully describe the missile damage effectiveness based on a single damage standard, considering the different damage level results formed by a single missile hitting the target, the damage effectiveness is characterized by the probability of occurrence of different damage level results. Missile identification from the perspective of multiple damage level standards is conducive to comprehensive inspection of missile damage effectiveness. In addition, in order to overcome the problem of insufficient information in small sample missile assembly tests, the Bayesian method is combined with the system contribution degree to fuse multi-source prior information in the identification test design, and a test scheme with full information utilization and controllable risks for both parties is designed. The example shows that compared with the quantitative characterization index of a single damage standard, this study can fully describe the missile damage effectiveness, and compared with the improved binomial distribution hypothesis test method, the superiority of this method is verified.

Journal of applied artificial intelligence.Vol. 1(2)
Openalex Percentile: Top 84%
Advanced Measurement and Detection Methods
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Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards — Haobang Liu, Tao Hu, et al. · Journal of applied artificial intelligence. (2024) | TGRS Research Map | TGRS