Network-based structure optimization method of the anti-aircraft system

doi:10.23919/JSEE.2023.000019

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (2): 374-395.doi: 10.23919/JSEE.2023.000019

• • 上一篇

收稿日期:2021-03-15 出版日期:2023-04-18 发布日期:2023-04-18

Network-based structure optimization method of the anti-aircraft system

Qingsong ZHAO¹^,*(), Junyi DING²(), Jichao LI¹(), Huachao LI¹(), Boyuan XIA¹

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
² International Office of Cooperation and Exchanges, National University of Defense Technology, Changsha 410073, China

Received:2021-03-15 Online:2023-04-18 Published:2023-04-18
Contact: Qingsong ZHAO E-mail:zhaoqingsong@nudt.edu.cn;Lexi.ding9512@gmail.com;ljcnudt@hotmail.com;llihuachao_nudt@163.com
About author:
ZHAO Qingsong was born in 1975. He received his M.S. and Ph.D. degrees from National University of Defense Technology, Changsha, China, in 2002 and 2008, respectively. He is a professor in National University of Defense Technology. He was a visiting scholar with the Department of Technology Policy and Management, Delft University of Technology, Netherlands. His research interests include system integration, defense acquisition and system-of-systems architecture design and optimization, and complex system analysis. E-mail: zhaoqingsong@nudt.edu.cn

DING Junyi was born in 1995. She received her M.S. degree from National University of Defense Technology, Changsha, China, in 2019. She is a program manager at the International Office of Cooperation and Exchanges, National University of Defense Technology. Her research interests include system-of-systems modeling and evaluation, network modeling, and temporal constraint resolving. E-mail: Lexi.ding9512@gmail.com

LI Jichao was born in 1990. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, in 2019. He is an associate professor in National University of Defense Technology. His research interests focus on studying complex systems with a combination of theoretical tool and data analysis, including mathematical modeling of heterogeneous information networks, applying network methodologies to analyze the development of complex system-of-systems, and data-driven studying of the collective behavior of humans. E-mail: ljcnudt@hotmail.com

LI Huachao was born in 1994. He received his B.S. degree from National University of Defense Technology, Changsha, China, in 2022. He is a M.S. candidate in National University of Defense Technology. His research interests include system of systems engineering, complex systems, and systems evaluation and optimization. E-mail: llihuachao_nudt@163.com

XIA Boyua was born in 1994. He received his M.S. degree from National University of Defense Technology, Changsha, China, in 2017. He is a Ph.D. candidate in National University of Defense Technology. His research interests include defense acquisition, complex systems, and system portfolio selection and optimization. E-mail: xiaboyuan@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (72071206)

摘要/Abstract

Abstract:

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

Key words: anti-aircraft system, optimization, combat network model (CNM), causal strength (CAST) logic, influence network (IN), time constraint network (TCN)

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 374-395.

Qingsong ZHAO, Junyi DING, Jichao LI, Huachao LI, Boyuan XIA. Network-based structure optimization method of the anti-aircraft system[J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 374-395.

图/表 34

参考文献 48

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Node type	S	D	I	T
S	S→S	S→D	S→I	S→T
D	D→S	D→D	D→I	D→T
I	I→S	I→D	I→I	I→T
T	T→S	T→D	T→I	T→T

Network edge	Meaning
$T\xrightarrow{ {{\rm{scout}}} }S$	A sensor scouts a target and acquires its intelligence
$ S\xrightarrow{{{\text{share}}}}S $	A sensor shares intelligence with another sensor entity
$ S\xrightarrow{{{\text{upload}}}}D $	A sensor uploads reconnaissance information to a decider entity
$ D\xrightarrow{{{\text{command}}}}D $	One decider entity receives a control command from a decider entity
$D\xrightarrow{ {{\rm{command}}} }S$	One sensor entity receives the control command from a decider entity
$D\xrightarrow{ {{\rm{command}}} }I$	An influencer entity receives control command from a decider entity
$I\xrightarrow{ {{\rm{attack}}} }T$	An influencer entity attacks a tatget

Attack chain	Meaning
$ {T}\to {S}\to {D}\to {I}\to {T} $	The typical attack chain
$ {T}\to {S}\to {S}\to {D} $ $ \to {I}\to {T} $	The attack chain with sensors’ information sharing
$ {T}\to {S}\to {D}\to {D} $ $ \to {I}\to {T} $	The attack chain with deciders’ cooperative command
$ {T}\to {S}\to {S}\to {D} $ $ \to {D}\to {I}\to {T} $	The attack chain with both information sharing and cooperative command
$ {T}\to {S}\to {D}\to {S} $ $ \to {D}\to {I}\to {T} $	The attack chain experiencing information feedback
$ {T}\to {S}\to {S}\to {D} $ $ \to {S}\to {D}\to {I}\to {T} $	The attack chain with information sharing and experiences information feedback
$ {T}\to {S}\to {D}\to {D} $ $ \to {S}\to {D}\to {I}\to {T} $	The attack chain with cooperative command and experiences information feedback

Node type		Information chain
Node type		Function	Example
Sensor ( $ S $ )	Collector ( $ C $ )	Collecting information and detecting targets	EWS
	Processor ( $ P $ )	Processing information and forming original combat situation	SGS
	Transferor ( ${\rm{Tr}}$ )	Delivering information without amendment and other operation	CRS
Decider ( $ D $ )	User ( $ U $ )	Using information to form a combat plan	JOC
Decider ( $ D $ )	Transferor ( ${\rm{Tr}}$ )	Delivering information without amendment and other operation	Destroyer
Influencer ( $ I $ )	Influencer ( $ I $ )	Destroying targets with firepower	SAMWS
Target ( $ T $ )	Ø	Ø	Ø

Type	Combat entity	Combat activity
Sensor ( $ S $ )	EWS Ⅰ	Search for target
	EWS Ⅱ	Search for target
	EWR	Search for target
	AWACS	Search for target
	SGS	Process data
	CRS	Transfer data
Decider ( $ D $ )	JOC	Form combat plan
	WFC	Form combat plan
	Destroyer	Transfer command
	Shipborne fighter (SF) Ⅰ	Execute command
	SF Ⅱ	Execute command
Influencer ( $ I $ )	GBMWS	Strike the target
	AAMWS Ⅰ	Strike the target
	AAMWS Ⅱ	Strike the target
	SAMWS	Strike the target

Network-based structure optimization method of the anti-aircraft system

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Algorithm	Initial population	Cross probability	Mutation possibility	Pareto solution
SPEA2	50	0.85	0.05	11
	100	0.85	0.05	11
	150	0.85	0.05	13
NSGA2	50	0.85	0.05	25
	100	0.85	0.05	37
	150	0.85	0.05	43
NSGA3	50	0.85	0.05	10
	100	0.85	0.05	17
	150	0.85	0.05	18

	EWSⅠ	EWSⅡ	EWR	AWACS	SGS	JOC	WFC	CRS	Destroyer	SFⅠ	SFⅡ	GBMWS	AAMWS	AAMWS	SAMWS
EWSⅠ					1			1
EWSⅡ					1			1
EWR			1		1		1
AWACS					1				1
SGS						1		1	1	1
JOC							1					1
WFC									1
CRS	1	1			1					1
Destroyer										1	1				1
SFⅠ													1
SFⅡ														1
GBMWS	1
AAMWSⅠ										1
AAMWSⅡ
SAMWS