Approach for air-to-air confrontment based on uncertain interval information conditions

doi:10.21629/JSEE.2019.01.10

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (1): 100-109.doi: 10.21629/JSEE.2019.01.10

• Systems Engineering • Previous Articles Next Articles

Approach for air-to-air confrontment based on uncertain interval information conditions

Qiuni LI¹(), Rennong YANG¹(), Chao FENG²(), Zongcheng LIU^1,*()

¹ Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China
² Unit 66133 of the PLA, Beijing 100000, China

Received:2017-11-06 Online:2019-02-27 Published:2019-02-27
Contact: Zongcheng LIU E-mail:lqnjk1@126.com;yangrn6907@foxmail.com;1216682261@qq.com;liu434853780@163.com
About author:LI Qiuni was born in 1985. She received her M.S. degree from Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, China in 2013. She is currently pursuing her Ph.D. degree at Air Force Engineering University. Her research interests include game theory, multi-agent systems, multi-objective optimization, particularly modeling, simulation and its application. E-mail:lqnjk1@126.com|YANG Rennong was born in 1969. He received his M.S. degree 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|FENG Chao was born in 1991. He received his M.S. degree from Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an, China in 2016. His research interests include multi-objective optimization and collaborative planning, particularly modeling, and simulation. E-mail:1216682261@qq.com|LIU Zongcheng was born in 1987. He received his B.Sc. degree in electrical engineering and automation from Air Force Engineering University, Xi'an, China in 2009, and his M.Sc. and Ph.D. degrees in control theory and engineering from Air Force Engineering University, in 2011 and 2015, respectively. He is currently a lecturer in Air Force Engineering University. His research interests include flight control, intelligent and autonomous control and neural networks. E-mail:liu434853780@163.com
Supported by:
the National Natural Science Foundation of China(61603411);the National Natural Science Foundation of China(60573172);the National Natural Science Foundation of China(50875132);This work was supported by the National Natural Science Foundation of China (61603411; 60573172; 50875132)

Abstract

Abstract:

Model combat information conditions are always uncertain and varied, the uncertain interval theory is therefore introduced into the research of multiple fighters suppressing the integrated air defense system (IADS) problem. Considering that the combat information conditions are uncertain intervals, the payoff function of the game for multiple fighters suppressing the IADS is modeled. Using the operation rules for interval numbers and the possibility degree, an improved chaotic particle swarm optimization (CPSO) is designed to solve the proposed model so as to obtain the optimal game solution. Comparison simulations are performed to analyze the influence of the weapons consumption and the distances of non-escaped zone and jamming on air combat result. Simulation results suggest that Nash equilibrium is achieved and verify the effectiveness of the proposed method.

Key words: interval information, integrated air defense system (IADS), air combat, chaotic particle swarm optimization (CPSO), game

Qiuni LI, Rennong YANG, Chao FENG, Zongcheng LIU. Approach for air-to-air confrontment based on uncertain interval information conditions[J]. Journal of Systems Engineering and Electronics, 2019, 30(1): 100-109.

Figures/Tables 4

Table 1

Table 2

Table 3

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References 20

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Value	Fighters(No.)
Value	1	2	3	4	5	6	7	8	9	10
Value(min)	0.30	0.20	0.34	0.24	0.64	0.42	0.42	0.21	0.45	0.12
Value(max)	0.80	0.35	0.45	0.82	0.72	0.87	0.77	0.74	0.65	0.52

Threat	Fighters(No.)
Threat	1	2	3	4	5	6	7	8	9	10
Threat(min)	0.7	0.2	0.7	0.4	0.3	0.2	0.5	0.4	0.4	0.3
Threat(max)	0.9	0.3	0.9	0.8	0.7	0.7	0.8	0.8	0.7	0.8

Condition of suppressing side	Condition of defensing side	Winning times (percentage of suppressing side)/%	Winning times (percentage of defensing side)/%	Draw times	Average time/s
Initial condition $(h_1 = 4, h_2 = 4, g_1 = 80\; {\rm{km}}, g_2 = 80\; {\rm{km}}, f_1 = 80\; {\rm{km}}, f_2 = 80\; {\rm{km}})$
		13/43.33	12/40.00	5	69.89
The first group experiments (The distance of non-escaped zone is adjusted)
$g_1 = 140$ km	$g_2 = 80$ km	29/96.67	1/3.33	0	62.52
$g_1 = 120$ km	$g_2 = 80$ km	28/93.33	1/3.33	1	65.29
$g_1 = 100$ km	$g_2 = 80$ km	26/86.67	2/6.67	2	69.11
The second group experiments (The weapons consumption is adjusted)
$h_1 = 7$	$h_2 = 4$	25/83.34	5/16.66	0	62.71
$h_1 = 6$	$h_2 = 4$	22/73.34	7/23.34	1	65.30
$h_1 = 5$	$h_2 = 4$	18/60.00	10/33.34	2	67.04
The third group experiments (The distance of jamming is adjusted)
$f_1 = 140$ km	$f_2 = 80$ km	19/63.34	11/36.67	0	70.13
$f_1 = 120$ km	$f_2 = 80$ km	18/60.00	9/30.00	3	70.79
$f_1 = 100$ km	$f_2 = 80$ km	18/60.00	11/36.67	1	74.91

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Approach for air-to-air confrontment based on uncertain interval information conditions

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