Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

doi:10.21629/JSEE.2018.01.09

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (1): 86-97.doi: 10.21629/JSEE.2018.01.09

• Systems Engineering • Previous Articles Next Articles

Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

Changqiang HUANG(), Kangsheng DONG(), Hanqiao HUANG*(), Shangqin TANG(), Zhuoran ZHANG()

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

Received:2017-01-18 Online:2018-02-26 Published:2018-02-23
Contact: Hanqiao HUANG E-mail:hcqxian@163.com;kgddks@163.com;cnxahhq@126.com;carnationtang2@163.com;zhuoran1009@163.com
About author:HUANG Changqiang was born in 1961. He received his Ph.D. degree in navigation, guidance and control from Northwestern Polytechnical University in 2006. He is a professor and doctoral tutor of Air Force Engineering University. He has worked in aerial weapon system and application engineering for more than 30 years. His current research is the autonomous air combat for unmanned combat aerial vehicle, artificial intelligence including knowledge extraction, big data application and air combat simulation system. E-mail: hcqxian@163.com|DONG Kangsheng was born in 1988. He received his M.S. degree in unmanned aircraft combat system and technology from Air Force Engineering University in 2013. He is now a Ph.D. candidate in the Aeronautics and Astronautics Engineering College of Air Force Engineering University. His research interests include autonomous air combat for unmanned combat aerial vehicle, artificial intelligence, optimization algorithms and its application in unmanned aerial vehicle combat system. E-mail: kgddks@163.com|HUANG Hanqiao was born in 1982. He received his Ph.D. degree in navigation, guidance and control from Northwestern Polytechnical University in 2010. He is now an associate professor of Air Force Engineering University and a postdoctor. His research interests include autonomous air combat for unmanned combat aerial vehicle, cooperative combat and air combat simulation. E-mail: cnxahhq@126.com|TANG Shangqin was born in 1984. He received his Ph.D. degree in armament science and technology from Air Force Engineering University in 2012. He is now a lecturer of Air Force Engineering University. His research interests include autonomous air combat for unmanned combat aerial vehicle, situation assessment and maneuver decision. E-mail: carnationtang2@163.com|ZHANG Zhuoran was born in 1990. He received his M.S. degree in unmanned aircraft combat system and technology from Air Force Engineering University in 2015. He is a Ph.D. candidate of Air Force Engineering University. His research interests include autonomous air combat for unmanned combat aerial vehicle, optimization algorithm and its application in unmanned aerial vehicle combat system. E-mail: zhuoran1009@163.com
Supported by:
the National Natural Science Foundation of China(61601505);the Aeronautical Science Foundation of China(20155196022);the Shaanxi Natural Science Foundation of China(2016JQ6050);This work was supported by the National Natural Science Foundation of China (61601505), the Aeronautical Science Foundation of China (20155196022) and the Shaanxi Natural Science Foundation of China (2016JQ6050)

Abstract

Abstract:

To reach a higher level of autonomy for unmanned combat aerial vehicle (UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system, the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result, adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty, to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.

Key words: autonomous air combat, maneuver decision, Bayesian inference, moving horizon optimization, situation assessment, fuzzy logic

Changqiang HUANG, Kangsheng DONG, Hanqiao HUANG, Shangqin TANG, Zhuoran ZHANG. Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization[J]. Journal of Systems Engineering and Electronics, 2018, 29(1): 86-97.

Figures/Tables 12

Fig 1

Fig 2

Fig 3

Fig 4

Table 1

Conditional likelihood functions of situation assessment states"

Situation for UCAV	${\it\Gamma} ^r$	Likelihood function	Range
Advantage	1	$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = (90^{\circ}-\vartheta _{k+1}^r)/90^{\circ}$	$0^{\circ}{\leqslant} \vartheta ^r < 90^{\circ}$
		$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = 0$	$90^{\circ}{\leqslant} \vartheta ^r{\leqslant} 180^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = -(\vartheta _{k+1}^b -90^{\circ})/90^{\circ}$	0$^{\circ}{\leqslant} \vartheta ^b < 90^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = 0$	$90^{\circ}{\leqslant} \vartheta ^b{\leqslant} 180^{\circ}$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = 1-d_{k+1} /D_{\rm adv} $	$[0, D_{\rm adv}]$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = 0$	$[D_{\rm adv}, \propto]$
Disadvantage	2	$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = 0$	$0^{\circ}{\leqslant} \vartheta ^r < 90^{\circ}$
		$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = -(90^{\circ}-\vartheta _{k+1}^r)/90^{\circ}$	$^{\circ}{\leqslant} \vartheta ^r{\leqslant} 180^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = (90^{\circ}-\vartheta _{k+1}^b)/90^{\circ}$	$^{\circ}{\leqslant} \vartheta ^b < 90^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = 0$	$90^{\circ}{\leqslant} \vartheta ^b{\leqslant} 180^{\circ}$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = 1-d_{k+1}/D_{\rm adv} $	$[0, D_{\rm adv}]$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = 0$	$[D_{\rm adv}, \propto]$
Mutual safe	3	$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = -(\vartheta _{k+1}^r-90^{\circ})/90^{\circ}$	$0^{\circ}{\leqslant} \vartheta ^r < 90^{\circ}$
		$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = 0$	$90^{\circ}{\leqslant} \vartheta ^r{\leqslant} 180^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = 0$	$0^{\circ}{\leqslant} \vartheta ^b < 90^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = (\vartheta _{k+1}^b -90^{\circ})/90^{\circ}$	$90^{\circ}{\leqslant} \vartheta ^b{\leqslant} 180^{\circ}$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = d_{k+1}/D_{\rm adv} $	$[0, D_{\rm adv}]$
		$p^d(d_{k+1} \|{\it\Gamma} _{k+1}^r = j) = 1$	$[D_{\rm adv}, \propto]$
Mutual disadvantage	4	$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = (90^{\circ}-\vartheta _{k+1}^r)/90^{\circ}$	$0^{\circ}{\leqslant} \vartheta ^r < 90^{\circ}$
		$p^{\vartheta ^r}(\vartheta _{k+1}^r \|{\it\Gamma} _{k+1}^r = j) = 0$	$90^{\circ}{\leqslant} \vartheta ^r{\leqslant} 180^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = 90-\vartheta _{k+1}^b /90$	$0^{\circ}{\leqslant} \vartheta ^b < 90^{\circ}$
		$p^{\vartheta ^b}(\vartheta _{k+1}^b \|{\it\Gamma} _{k+1}^r = j) = (\vartheta _{k+1}^b -90)/90$	$90^{\circ}{\leqslant} \vartheta ^b{\leqslant} 180^{\circ}$
		$p^d(d_{k+1}\|{\it\Gamma} _{k+1}^r = j) = d_{k+1}/D_{\rm adv} $	$[0, D_{\rm adv}]$
		$p^d(d_{k+1}\|{\it\Gamma} _{k+1}^r = j) = 0$	$[D_{\rm adv}, \propto]$

Table 1

Table 2

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Fig 9

Fig 10

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Situation	$\eta _1 $	$\eta _2 $	$\eta _3 $	$\eta _4 $
Advantage	0.4	0.1	0.4	0.1
Disadvantage	0.4	0	0.5	0.1
Mutual safe	0.6	0.15	0.1	0.15
Mutual disadvantage	0.4	0.3	0	0.3

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Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization

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