天基信息支援体系建模与效能评估方法

doi:10.12305/j.issn.1001-506X.2024.10.18

Abstract

Abstract:

Aiming at the problem of strong subjectivity and high complexity in the effectiveness evaluation of space-based information support system, a projection gradient neural network-based effectiveness evaluation method for space-based information support system is proposed. Firstly, based on the Department of Defense Architecture Framework (DoDAF) optical products and observation-orientation-decision-action (OODA) loop are used to sort out the system operational process, and then the evaluation index system is established, and the data samples for effectiveness evaluation are generated based on the discrete-event simulation. Then, the effectiveness evaluation agent model is constructed based on the Rosen-back propagation (BP) neural network, and the restriction of the weight parameter is used to solve the problem that the evaluation model is difficult to be interpreted under the efficiency-type indexes. Finally, a validation test of the evaluation model is conducted on the simulation samples, and the results show that the proposed method can improve the computational performance by more than 50% compared with the traditional BP neural network in the effectiveness evaluation of space-based information support systems, which can provide technical support for the evaluation of the effectiveness of space-based information support systems.

Key words: space-based information support system, neural network, projection gradient method, effectiveness evaluation

CLC Number:

Yu CHEN, Peng SHI, Li MA, Wenlong LI. Modeling and effectiveness evaluation method of space-based information support system[J]. Systems Engineering and Electronics, 2024, 46(10): 3407-3415.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 1

Table 2

Fig.11

Fig.12

References 22

1	刘欣, 张占月, 吴俊娴, 等. 美军天基信息支援空中进攻作战研究[J]. 火力与指挥控制, 2021, 46 (7): 118-123, 129.
	LIU X , ZHANG Z Y , WU J X , et al. Study on airborne attack operations supported by US space-based information system[J]. Firepower & Command Control, 2021, 46 (7): 118-123, 129.
2	于小岚, 熊伟. 基于DBN的天基信息支援装备体系作战效能评估方法[J]. 指挥控制与仿真, 2023, 45 (4): 14- 23.
	YU X L , XIONG W . Combat effectiveness evaluation method of space-based information support equipment system based on DBN[J]. Command Control & Simulation, 2022, 45 (4): 14- 23.
3	郭齐胜, 张磊. 武器装备系统效能评估方法研究综述[J]. 计算机仿真, 2013, 30 (8): 1-4, 18.
	GUO Q S , ZHANG L . A review of evaluation methods for weapon and equipment system effectiveness[J]. Computer Simulation, 2013, 30 (8): 1-4, 18.
4	魏武臣, 徐硕, 何晓夫, 等. 基于作战仿真推演的空-地制导弹药效能评估[J]. 兵工学报, 2022, 43 (1): 107- 114.
	WEI W C , XU S , HE X F , et al. Effectiveness evaluation of air-ground guided munitions based on operational simulation[J]. Acta Armamentarii, 2022, 43 (1): 107- 114.
5	姜剑雄, 孔祥龙, 师鹏, 等. 基于ANP的天基海洋监视体系作战能力评估方法[J]. 火力与指挥控制, 2017, 42 (10): 53- 58.
	JIANG J X , KONG X L , SHI P , et al. Operational capability evaluation method of space-based ocean surveillance system based on ANP[J]. Firepower & Command Control, 2017, 42 (10): 53- 58.
6	王双川, 胡起伟, 白永生, 等. 基于非对称灰色云模型的装备维修保障系统效能评估[J]. 兵工学报, 2019, 40 (6): 1277- 1286.
	WANG S C , HU Q W , BAI Y S , et al. Performance evaluation of equipment maintenance and support system based on asymmetric grey cloud model[J]. Acta Armamentarii, 2019, 40 (6): 1277- 1286.
7	戚宗锋, 王华兵, 李建勋. 基于深度学习的雷达侦察系统作战能力评估方法[J]. 指挥控制与仿真, 2020, 42 (2): 59- 64.
	QI Z F , WANG H B , LI J X . Operational capability evaluation method of radar reconnaissance system based on deep learning[J]. Command Control & Simulation, 2020, 42 (2): 59- 64.
8	LEI M , DONG Y F , LI Z , et al. Application of a modified BPS neural network based on three-way decision theory in an effectiveness evaluation for a remote sensing satellite cluster[J]. Remote Sensing, 2023, 15 (13): 3305. doi: 10.3390/rs15133305
9	WANG S , DU Y H , ZHAO S J , et al. Research on the construction of weaponry indicator system and intelligent evaluation methods[J]. Scientific Reports, 2023, 13 (1): 19370. doi: 10.1038/s41598-023-46660-5
10	刘德胜, 马宝林, 葛亚维. 作战体系建模方法与应用[J]. 指挥控制与仿真, 2020, 42 (5): 1- 6.
	LIU D S , MA B L , GE Y W . Modeling methods and applications for operational systems[J]. Command Control & Simulation, 2020, 42 (5): 1- 6.
11	LYU H N, XIE J, LIU K. Research on the architecture of shipborne UAV target indication command and control system based on DoDAF[C]//Proc. of the 2nd International Confe-rence on Computer Engineering and Intelligent Control, 2021: 162-171.
12	王新尧, 曹云峰, 孙厚俊, 等. 基于DoDAF的有人/无人机协同作战体系结构建模[J]. 系统工程与电子技术, 2020, 42 (10): 2265- 2274. doi: 10.3969/j.issn.1001-506X.2020.10.15
	WANG X Y , CAO Y F , SUN H J , et al. Modeling of manned/unmanned aircraft cooperative combat system architecture based on DoDAF[J]. Systems Engineering and Electronics, 2020, 42 (10): 2265- 2274. doi: 10.3969/j.issn.1001-506X.2020.10.15
13	周琛, 尚柏林, 宋笔锋, 等. 基于作战环的航空武器装备体系贡献率评估[J]. 航空学报, 2022, 43 (2): 309- 320.
	ZHOU C , SHANG B L , SONG B F , et al. Contribution rate eva-luation of aviation weapon equipment system based on operational environment[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (2): 309- 320.
14	WANG Z , LIU S F , FANG Z G . Research on SoS-GERT network model for equipment system of systems contribution eva-luation based on joint operation[J]. IEEE Systems Journal, 2020, 14 (3): 4188- 4196.
15	JOSLYN C A, AKSOY S, CALLAHAN T J, et al. Hypernetwork science: from multidimensional networks to computational topology[EB/OL]. [2023-10-27]. https://arxiv.org/abs/2003.11782.
16	鲁统亮, 陈文豪, 葛冰峰, 等. 信息支援下的作战体系多层网络建模[J]. 系统工程与电子技术, 2022, 44 (2): 520- 528. doi: 10.12305/j.issn.1001-506X.2022.02.21
	LU T L , CHEN W H , GE B F , et al. Multi-layer network modeling of operational system based on information support[J]. Systems Engineering and Electronics, 2022, 44 (2): 520- 528. doi: 10.12305/j.issn.1001-506X.2022.02.21
17	SHAYMAA E M , OKASHA A E , HOSNY H A . Agent-based models of administrative corruption: an overview[J]. International Journal of Modeling and Simulation, 2022, 42 (2): 350- 358.
18	李峻森, 方依宁, 张云安, 等. 面向任务的装备保障体系多Agent建模与评估方法[J]. 系统工程与电子技术, 2023, 45 (1): 279- 290. doi: 10.12305/j.issn.1001-506X.2023.01.33
	LI J S , FANG Y N , ZHANG Y A , et al. Multi-agent modeling and evaluation method of mission-oriented equipment support system[J]. Systems Engineering and Electronics, 2023, 45 (1): 279- 290. doi: 10.12305/j.issn.1001-506X.2023.01.33
19	曹延华, 杨海涛, 何宇, 等. 天基信息服务体系概论[M]. 北京: 国防工业出版社, 2018.
	CAO Y H , YANG H T , HE Y , et al. Introduction to space-based information service system[M]. Beijing: National Defense Industry Press, 2018.
20	陈俊霖, 侯麒麟, 宁文学, 等. 基于BP神经网络的火箭弹射击效率计算方法[J]. 火力与指挥控制, 2022, 47 (6): 87- 92.
	CHEN J L , HOU Q L , NING W X , et al. Calculation method of rocket firing efficiency based on BP neural network[J]. Fire Control & Command Control, 2022, 47 (6): 87- 92.
21	AHAMD D , MEHDI B , GEVORK B G . Highly accurate directional overcurrent coordination via combination of Rosen's gradient projection-complex method with GA-PSO algorithm[J]. IEEE Systems Journal, 2020, 14 (1): 1171- 1182.
22	马力, 师鹏, 陈宇, 等. 天基信息支援体系离散事件仿真与效能评估[J]. 系统工程与电子技术, 2024, 46 (3): 906- 913. doi: 10.12305/j.issn.1001-506X.2024.03.16
	MA L , SHI P , CHEN Y , et al. Discrete event simulation and effectiveness evaluation of space-based information support system[J]. Systems Engineering and Electronics, 2024, 46 (3): 906- 913. doi: 10.12305/j.issn.1001-506X.2024.03.16

序号	A1	A2	A3	B1	B2	C1	C2	C3	D1	D2	E
1	0.000	0.229	0.913	0.152	0.750	0.538	1.000	0.078	0.443	0.200	0.602
2	0.000	0.399	0.259	0.800	0.500	0.911	0.167	0.264	0.146	0.200	0.001
3	0.500	0.351	0.513	0.402	0.000	0.240	0.167	0.184	0.240	0.400	0.011
4	0.250	0.900	0.369	0.111	0.750	0.390	0.167	0.403	0.097	0.200	0.298
5	0.250	0.821	0.015	0.043	0.250	0.649	0.667	0.648	0.451	0.600	0.239
6	0.250	0.626	0.780	0.081	1.000	0.776	0.500	0.436	0.447	0.400	0.444
$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$
1 100	0.750	0.139	0.392	0.318	0.750	0.108	1.000	0.413	0.779	0.400	0.022

序号	A1	A2	A3	B1	B2	C1	C2	C3	D1	D2	E
1	0.250	0.570	0.294	0.349	0.500	0.902	0.500	0.055	0.177	0.400	0.231
2	0.750	0.863	0.354	0.396	0.000	0.577	0.667	0.911	0.498	0.400	0.060
3	0.500	0.075	0.972	0.373	0.000	0.246	0.833	0.694	0.240	0.800	0.012
4	0.750	0.622	0.294	0.558	0.000	0.222	0.500	0.599	0.886	0.200	0.064
5	0.250	0.513	0.461	0.661	0.250	0.544	0.167	0.419	0.629	0.600	0.003
6	0.250	0.432	0.418	0.908	0.250	0.985	0.167	0.723	0.857	0.200	0.022
$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$	$ \vdots$
100	0.250	0.774	0.343	0.063	0.750	0.598	0.000	0.793	0.798	1.000	0.178

[1]	Xiaobin LI, Dong XU, Xue YANG. Trajectory tracking control with predefined dynamic performance for underactuated autonomous underwater vehicle [J]. Systems Engineering and Electronics, 2024, 46(9): 3185-3197.
[2]	Tianqi ZHANG, Zongfang YANG, Han ZOU, Kunran MA. Blind identification algorithm for polarization code parameters based on encoding matrix estimation [J]. Systems Engineering and Electronics, 2024, 46(9): 3221-3230.
[3]	Lei WANG, Jin ZHANG, Qiuxuan YE. Spectrum sensing method based on cyclic spectrum and residual neural network in LDACS system [J]. Systems Engineering and Electronics, 2024, 46(9): 3231-3238.
[4]	Ruibin ZHANG, Mengtao ZHU, Yunjie LI. Radar transmitting signal generation method for modulation recognition network stealth [J]. Systems Engineering and Electronics, 2024, 46(7): 2256-2268.
[5]	Bing QI, Jianhua CHENG, Yanchi ZHAO, Zili WANG. Precise temperature drift error estimation method for capacitive MEMS accelerometers based on micro-deformation analysis [J]. Systems Engineering and Electronics, 2024, 46(7): 2437-2445.
[6]	Weiyi WU, Yunxian JIA, Xiangzheng JIANG, Xianming SHI, Jie LIU, Bin LIU, Enzhi DONG, Xi ZHU. Method for determining for carrying material varieties of stage task [J]. Systems Engineering and Electronics, 2024, 46(6): 2054-2064.
[7]	Hao QING, Zhigeng FANG, Yuhong WANG, Xirui QIU. Combination prediction of civil aircraft demand based on grey-neural network [J]. Systems Engineering and Electronics, 2024, 46(5): 1665-1672.
[8]	Zongfang YANG, Tianqi ZHANG, Kunran MA, Han ZOU. Blind identification of channel coding types based on deep neural networks [J]. Systems Engineering and Electronics, 2024, 46(5): 1820-1829.
[9]	Lisha ZHENG, Dongliang YIN, Xuan WANG. Operational effectiveness evaluation of phased array radar based on improved D-S evidence theory [J]. Systems Engineering and Electronics, 2024, 46(4): 1330-1336.
[10]	Tong HE, Qing LU, Jun ZHOU, Zongyi GUO. Line-of-sight angle constraint guidance with neural network interference observer [J]. Systems Engineering and Electronics, 2024, 46(4): 1372-1382.
[11]	Hongjin ZHOU, Hui SONG, Wenliang FAN, Su WANG, Dongliang GU. Ship inertial navigation system position correction method based on Bayesian neural network [J]. Systems Engineering and Electronics, 2024, 46(4): 1393-1400.
[12]	Xianpeng MENG, Limin LIU, Jian DONG, Li WANG, Wenhua HU. Radar frequency agility behavior recognition based on bi-cell recurrent neural network [J]. Systems Engineering and Electronics, 2024, 46(3): 898-905.
[13]	Li MA, Peng SHI, Yu CHEN, Wenlong LI. Discrete event simulation and effectiveness evaluation of space-based information support system [J]. Systems Engineering and Electronics, 2024, 46(3): 906-913.
[14]	Lei YU, Qiuyue DENG, Liying ZHENG, Haoyu WU. Second-order progressive feature fusion network for image super-resolution reconstruction [J]. Systems Engineering and Electronics, 2024, 46(2): 391-400.
[15]	Tao HU, Liqun SHEN, Jingda ZHU, Chenghui SUN, Weifeng DONG. Sensitivity analysis of radar system effectiveness based on FAST and Sobol index method [J]. Systems Engineering and Electronics, 2024, 46(2): 561-569.

Modeling and effectiveness evaluation method of space-based information support system

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 22

Related Articles 15

Recommended Articles

Metrics

Comments