Air Combat Situation Assessment by Gray Fuzzy Bayesian Network

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Abstract:

Automatic and accurate situation assessment is essential for aircraft to conduct and maintain operations autonomously and effectively. There are many uncertainties during the process of air combat situation assessment which have a significant influence on operational decision making. For the uncertainty of advanced aircraft in air combat Situation assessment, with the uncertainty knowledge representation of gray fuzzy theory and uncertainty reasoning of Bayesian network, the fuzzy information can change into the probability of domain knowledge through fuzzy probability conversion formula, A gray fuzzy Bayesian network model for situation assessment of air combat is established, the simulation results shows that the model is reasonable and feasible.

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114-119

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July 2011

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[1] G. L. Meng, G.H. Gong: Threat assessment of aerial targets based on hybrid Bayesian network[J]. Systems Engineering and Electronics, 2010, 32(11): 2398-2410.

Google Scholar

[2] A.J. Chen, Y.H. Shao, X.Y. Ren. The application research of gray fuzzy comprehensive evaluation in the university educational information [C]. Proceedings of the 1st International Workshop on Education Technology and Computer Science, ETCS 2009, 3: 293-297.

Google Scholar

[3] Koichi yamada: Probability-Possibility Transformation Based on Evidence Theory[C]. Proceedings of the IEEE International Fuzzy Systems Association World Congress, 2001, 1: 70-75.

Google Scholar

[4] Jousselme A: Uncertainty in a situation analysis perspective[C]. Proceedings of the International Society on Information Fusion Conference, 2003, 2: 1207-1214.

Google Scholar

[5] Z. L. Mu: Ground attack formation effectiveness evaluation method[D]. Air Force Engineering University, (2010).

Google Scholar

[6] C. Q. Kang, L.H. Guo, Y.C. Luo, X.Z. Wang: Model of situation and threat assessment based on fuzzy Bayesian network [J]. Opto-Electronic Engineering, 2008, 35(5): 1-5.

Google Scholar

[7] Z. Tang, X.G. Gao: Research on radiant point identification based on discrete dynamic Bayesian network [J]. Journal of System Simulation, 2009, 21(1): 117-126.

Google Scholar

[8] J.G. Shi, X.G. Gao, X.M. Li: Modeling air combat situation assessment by using fuzzy dynamic Bayesian network[J]. Journal of System Simulation, 2006, 18(5): 1093-1100.

Google Scholar