Situation assessment for air combat based on novel semi-supervised naive Bayes

doi:10.21629/JSEE.2018.04.11

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (4): 768-779.doi: 10.21629/JSEE.2018.04.11

• Systems Engineering • Previous Articles Next Articles

Situation assessment for air combat based on novel semi-supervised naive Bayes

Ximeng XU*(), Rennong YANG(), Ying FU()

Aeronautics and Astronautic Engineering College, Air Force Engineering University, Xi'an 710038, China

Received:2017-05-15 Online:2018-08-01 Published:2018-08-30
Contact: Ximeng XU E-mail:15398005756@163.com;yangrn6907@foxmail.com;15339178923@163.com
About author:XU Ximeng was born in 1990. He received his M.S. degree in weapon science and technology from Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, China. He is currently a Ph.D. in Aeronautics and Astronautics Engineering College, Air Force Engineering University. He has about 10 publications in journals and conferences. His main research interests include intelligent air combat decisions and applications. E-mail: 15398005756@163.com|YANG Rennong was born in 1969. He received his M.S. degree in systems engineering from Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, China. He is currently a professor in Aeronautics and Astronautics Engineering College, Air Force Engineering University. His main research interests include game theory, complex networks theory and optimization theory and applications. E-mail: yangrn6907@foxmail.com|FU Ying was born in 1993. She received her M.S. degree in communication engineering from Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, China. She is currently a Ph.D. in Aeronautics and Astronautics Engineering College, Air Force Engineering University. She has about six publications in journals and conferences. Her main research interests include electronic countermeasure and radar identification. E-mail: 15339178923@163.com
Supported by:
the Aviation Science Foundation of China(20152096019);This work was supported by the Aviation Science Foundation of China (20152096019)

Abstract

Abstract:

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots' description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

Key words: air combat, situation assessment, air combat maneuvering instrument, semi-supervised, naive Bayes

Ximeng XU, Rennong YANG, Ying FU. Situation assessment for air combat based on novel semi-supervised naive Bayes[J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 768-779.

Figures/Tables 12

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References 27

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Symbol	Name	Range
${\varphi _T}$	Azimuthal angle of target aircraft	$\left[ {{\rm{- \mathit{ π} , \mathit{ π} }}} \right]$
${q_T}$	Entrance angle of target aircraft	$\left[ {{\rm{- \mathit{ π} , \mathit{ π} }}} \right]$
${\varphi _F}$	Azimuthal angle of our aircraft	$\left[ {{\rm{- \mathit{ π} , \mathit{ π} }}} \right]$
${q_F}$	Entrance angle of our aircraft	$\left[ {{\rm{- \mathit{ π} , \mathit{ π} }}} \right]$
$R$	Relative distance of dual aircraft	—
$H$	Relative altitude of dual aircraft	—
${V_T}$	Velocity of target aircraft	—
${V_F}$	Velocity of our aircraft	—

$ K $	NSNB		SNB		NB
$ K $	Testaccuracy/%	Executiontime/ms	Testaccuracy/%	Executiontime/ms		Testaccuracy/%	Executiontime/ms
25	54.55	1 443	51.32	1 334		47.62	1 227
50	63.47	1 572	60.83	1 412		47.62	1 227
75	68.35	1 677	54.47	1 543		47.62	1 227
100	61.26	1 783	43.35	1 672		47.62	1 227

$ K $	NSNB		SNB		NB
$ K $	Testaccuracy/%	Executiontime/ms	Testaccuracy/%	Executiontime/ms		Testaccuracy/%	Executiontime/ms
25	75.26	1 534	71.37	1 416		68.52	1 352
50	78.13	1 613	77.63	1 507		68.52	1 352
75	83.52	1 732	75.28	1 622		68.52	1 352
100	88.64	1 864	73.14	1 742		68.52	1 352

$ K $	NSNB		SNB
$ K $	Testaccuracy/%	Executiontime/ms	Testaccuracy/%	Executiontime/ms
25	20.71	91	20.05	82
50	14.39	41	16.80	95
75	15.17	55	20.81	79
100	27.38	81	29.79	70

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Situation assessment for air combat based on novel semi-supervised naive Bayes

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