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

收稿日期:2017-05-15 出版日期:2018-08-01 发布日期:2018-08-30

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)

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 768-779.

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.

图/表 12

参考文献 27

1	ENDSLEY M R. Toward a theory of situation awareness in dynamic systems. Human Factors, 1995, 35 (1): 32- 45. doi: 10.1518/001872095779049543
2	WU W H, ZHOU S Y. Improvements of situation assessment for beyond-visual-range air combat based on missile launching envelope analysis. Systems Engineering and Electronics, 2011, 33 (12): 2679- 2685.
3	SHI J G, GAO X G. Modeling air combat situation assessment by using fuzzy dynamic Bayesian network. Journal of System Simulation, 2006, 18 (5): 1093- 1097.
4	JESSE L A, KALITA J K. Some computational approaches for situation assessment and impact assessment. KnowledgeBased Systems, 1997, 10, 87- 102. doi: 10.1109/ICIF.2002.1021221
5	AZIMIRAD E, HADDADNIA J. Target threat assessment using fuzzy sets theory. International Journal of Advances in Intelligent Informatics, 2015, 1 (2): 57- 74. doi: 10.26555/ijain.v1i2.18
6	RAO N P, KASHYAP S K, GIRIJA G. Situation assessment in air-combat: a fuzzy-Bayesian hybrid approach. Proc. of the International Conference on Aerospace Science and Technology, 2008: 26-28.
7	WU W H, ZHOU S Y. A new situation assessment model for modern within-visual-range air combat. Procedia Engineering, 2012, 29, 339- 343. doi: 10.1016/j.proeng.2011.12.719
8	GAO Y, XIANG J W. New threat assessment non-parameter model in beyond-visual-range air combat. Journal of System Simulation, 2006, 18 (9): 2570- 2573.
9	XIAO B S, FANG Y W, HU S G, et al. New threat assessment method in beyond-the-horizon range air combat. Systems Engineering and Electronics, 2009, 31 (9): 2163- 2166.
10	XIAO F, HUANG Y, LU W Z. A new model of the comprehensive advantages threat assessment in air combat simulation. Journal of Sichuan Ordnance, 2009, 30 (6): 53- 55.
11	YIN S, ZHU X P, QIU J B, et al. State estimation in nonlinear system using sequential evolutionary filter. IEEE Trans. on Industrial Electronics, 2016, 63(6): 3786-3794.
12	OU A H, ZHU Z Q. A method of threat assessment based on MADM and results of situation assessment in air to air combat. Fire Control Radar Technology, 2006, 35 (6): 64- 67.
13	NGUYEN X T. Threat assessment in tactical airborne environments. Proc. of the 5th International Conference on Information Fusion, 2002: 1300-1307.
14	WANG X H, QIN Z, LIU Y, et al. RBF neural network for threat sequencing. Journal of System Simulation, 2004, 16 (7): 1576- 1579.
15	FU Z W, KOU Y X, WANG L, et al. Multi-target threat assessment of air combat based on synthesis fuzzy assessment method. Electronics Optics & Control, 2009, 16 (9): 29- 32.
16	MAHONEY S M, LASKEY K B. Constructing situation specific belief networks. Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, 1997: 370-378.
17	MJOLSNESS E, DECCOSTE D. Machine learning for science: state of the art and future prospects. Science, 2001, 293 (5537): 2051- 2055. doi: 10.1126/science.293.5537.2051
18	UCI machine learning repository [DB/OL]. [2013-05-20]. http://archive.ics.uci.edu/ml/index.html.
19	RUSSELL S J, NORVIG P. Artificial intelligence: a modern approach prentice hall. New Jersey: Upper Saddle River, 2003.
20	ZUO J L, YANG R N, ZHANG Y, et al. Reconstruction and evaluation of close air combat decision making process based on fuzzy clustering. Acta Aeronautica et Astro Nautica Sinica, 2015, 36 (5): 1650- 1660.
21	SU J, SHIRAB J S, MATWIN S. Large scale text classification using semi-supervised multinomial naive Bayes. Proc. of the 28th International Conference on Machine Learning, 2011: 97-104.
22	GU J J, LIU W H. WVR air combat situation assessment model based on weapon engagement zone and kill probability. Systems Engineering and Electronics, 2015, 37 (6): 1306- 1312.
23	MA Y F, GONG H G, PENG X Y. Cognition behavior model for air combat based on reinforcement learning. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36 (4): 379- 383.
24	ZHANG C J, LI L L. Improving multilateration algorithm based on fuzzy C-means cluster in WSN. Electronic Design Engineering, 2016, 24 (8): 58- 61.
25	ZHANG H, SHENG S. Learning weighted naive Bayes with accurate ranking. Proc. of the 4th IEEE International Conference on Data Mining, 2004: 567-570.
26	MANN G S, MCCALLUM A. Generalized expectation criteria for semi-supervised learning with weakly labeled data. The Journal of Machine Learning Research, 2010, 11 (6): 955- 984.
27	DONG L Y, SUI P, SUN P, et al. Novel naive Bayes classification algorithm based on semi-supervised learning. Journal of Jilin University (Engineering and Technology Edition), 2016, 46 (3): 885- 886. doi: 10.13229/j.cnki.jdxbgxb201603031

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

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

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