Battle damage assessment method based on BN-cloud model

被引：0

作者：

Qu W.-J. ^{[1
]}

Xu Z.-L. ^{[1
]}

Zhang B.-L. ^{[2
]}

Liu Y. ^{[1
]}

机构：

[1] Aviation University of Air Force, Changchun, 130022, Jilin

[2] Unit 94810 of PLA, Nanjing, 211500, Jiangsu

来源：

Binggong Xuebao/Acta Armamentarii | 2016年 / 37卷 / 11期

关键词：

Bayesian network; Cloud model; Damage assessment; Monte Carlo algorithm; Ordnance science and technology; Radar;

D O I：

10.3969/j.issn.1000-1093.2016.11.016

中图分类号：

学科分类号：

摘要：

A new damage assessment method based on BN-Cloud model is put forward for the impact of complex and uncertain factors in modern battlefield environment. The characteristics of targets are analyzed, and a hierarchical evaluation index system is established, which is used to build a Bayesian network (BN) structure. Monte Carlo method is introduced into parameter learning, a condition probability table (CPT) of each network node is obtained through simulation, and the probability of each damage level is obtained by using the network inference. Finally, cloud model is used to transform target damage probability into real damage value so as to realize the transformation from uncertainty to certainty. The target physical damage degree is used as one of sub-nodes of functional damage degree in BN, in which CPT is used as a link. As a result, a new method to transform physical damage into function damage is realized. A radar target is taken as an example for simulation. The simulated result shows that the proposed method can be effectively used for target damage assessment, and achieves a significant improvement in accuracy and reliability compared with the existing algorithms. © 2016, Editorial Board of Acta Armamentarii. All right reserved.

引用

页码：2075 / 2084

页数：9

共 16 条

[1]

Jiang H., Chen H.-G., Xing X.-H., Damage assessment of airport target based on two level fuzzy comprehensive evaluation, Ordnance Industry Automation, 26, 8, pp. 1-2, (2007)

[2]

Li X.H., Ci L.L., Liu J.H., Et al., Research on battle damage assessment, 7th International Symposium on Test and Measurement, (2007)

[3]

Hu H., Yuan Z.-Y., Xie C.-S., Et al., Research on damage assessment model of system based on damage tree, Ship Electronic Engineering, 30, 8, pp. 32-35, (2010)

[4]

Qin Q.-F., Xu Z., Cao C.-G., Military effect evaluation of operational plan based on graph theory, Computer Science, 32, 7, pp. 148-151, (2005)

[5]

Li W.-X., Huang C.-Q., Wu W.-C., Et al., Battle damage assessment of air-to-ground precision guided weapon air-to-ground attack, System Engineering-Theory & Practice, 32, 1, pp. 211-218, (2012)

[6]

Jin Y., Hu Y.-A., Zhang J., Et al., Electomagnetic damage assessment of airborne radar based on bayesian networks, Transactions of Beijing Institute of Technology, 23, 9, pp. 960-975, (2012)

[7]

Soles A.M., Walsh M.R., Scott E.B., Damage detection and remediation system and methods thereof

[8]

Sui H.-G., Hua F., Fan Y.-D., Et al., Road damage extraction from high-resolution SAR image based on GIS data and Bayes network, Geomatics and Information Science of Wuhan University, 41, 5, pp. 578-583, (2016)

[9]

Wu Z.-L., Zhao Z.-S., Research on fire damage assessment model based on adaptive fuzzy neural network system, Acta Armamentarii, 33, 11, pp. 1352-1357, (2012)

[10]

Ni M., Peng Y.-Q., Luo Y., The model of prior evaluation of artillery firepower bombardment based on neural network and DEA, Fire Control & Command Control, 37, 2, pp. 127-131, (2012)

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