基于体系破击的联合火力打击目标价值评估

doi:10.3969/j.issn.1001-506X.2020.12.17

Abstract

Abstract:

The entropy weight ideal point method suitable for the multi-attribute index fusion is designed for the target selection problem in the system-of-systems tactics. Based on the super-network architecture, the target system-of-systems value based on the information flow cycle efficiency is designed. The evaluation algorithm is designed. The system-of-systems value evaluation algorithm of target combination is designed for the characteristics of combined firepower multi-target synchronous strike. On the basis of the above algorithms, the target multi-attribute evaluation is realized, and the system-of-systems value evaluation based on the single target and target combination is realized. The experimental analysis shows that the method can quickly realize the quantitative evaluation of the combined firepower target, and use the quantitative data to verify the three basic principles that should be followed in the joint fire attack. The target attribute table and the system-of-systems association network is combined to find the key to the system-of-systems attack. The target or target combination, the input conditions are simple and easy, and have the basic conditions for engineering application.

Key words: joint firepower strike, target value assessment, system-of-systems attack, super-network, information flow loop algorithm

CLC Number:

TP391

Hao LIU, Yan XING, Shijie WU. Value evaluation of joint fire strike target based on system-of-systems attack[J]. Systems Engineering and Electronics, 2020, 42(12): 2802-2810.

Figures/Tables 9

Fig.1

Table 1

Table 2

Table 3

Table 4

Fig.2

Fig.3

Fig.4

Fig.5

References 37

1	姜志鹏, 张多林, 邢清华. 给定任务和模式下指挥体系节点重要性评估方法[J]. 火力与指挥控制, 2015, 40 (6): 10- 13.
	JIANG Z P , ZHANG D L , XING Q H . Research on evaluation method for node importance of command system-of-systems architecture based on given task and mode[J]. Fire Control & Command Control, 2015, 40 (6): 10- 13.
2	WANG Y , LI J , HUANG W L , et al. Dynamic weapon target assignment based on intuitionistic fuzzy entropy of discrete particle swarm[J]. China Communications, 2017, 14 (1): 169- 179.
3	ZHAO M , ZHAO L L , SU X H , et al. Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model[J]. International Journal of Machine Learning & Cybernetics, 2017, 8 (3): 765- 780.
4	司光亚, 王飞, 刘洋. 基于仿真大数据的体系分析方法研究[J]. 系统仿真学报, 2019, 31 (3): 511- 519.
	SI G Y , WANG F , LIU Y . System analysis method based on simulation big data[J]. Journal of System Simulation, 2019, 31 (3): 511- 519.
5	ZHOU Y L, LI X B, ZHU Y F, et al. A discrete particle swarm optimization algorithm applied in constrained static weapon-target assignment problem[C]//Proc.of the 12th World Congress on Intelligent Control and Automation (WCICA), 2016: 3118-3123.
6	AHNER D K , PARSON C R . Optimal multi-stage allocation of weapons to targets using adaptive dynamic programming[J]. Optimization Letters, 2015, 9 (8): 1689- 1701. doi: 10.1007/s11590-014-0823-x
7	SHI S N , SHUI P L . Detection of low-velocity and floating small targets in sea clutter via income reference particle filters[J]. Signal Processing, 2018, 148 (4): 78- 90.
8	王宏宇, 吴纬, 魏艳艳. 基于超网络模型武器装备体系抗毁性分析[J]. 系统工程与电子技术, 2017, 39 (8): 1782- 1787.
	WANG H Y , WU W , WEI Y Y . Weapon system-of systems invulnerability analysis based on super network model[J]. Systems Engineering and Electronics, 2017, 39 (8): 1782- 1787.
9	NG B, ROSENBERG L, NGUYEN S T N. Target detection in sea clutter using resonance based signal decomposition[C]//Proc.of the IEEE Radar Conference, 2016.
10	QIN H I , HAN J J , XIANG Z , et al. Infrared small moving target detection using sparse representation-based image decomposition[J]. Infrared Physics & Technology, 2016, 76 (2): 148- 156.
11	CHEN X L , CHEN W , WANG X , et al. Sparsity-optimized separation of body waves and ground-roll by constructing dictionaries using tunable Q-factor wavelet transforms with diffe-rent Q-factors[J]. Geophysical Journal Internationa1, 2017, 211 (1): 621- 636. doi: 10.1093/gji/ggx332
12	李尔玉, 龚建兴, 黄健, 等. 基于功能链的作战体系复杂网络节点重要性评价方法[J]. 指挥与控制学报, 2018, 4 (1): 42- 49.
	LI E Y , GONG J X , HUANG J , et al. Node importance analysis of complex networks for combat systems based o function chain[J]. Journal of Command and Control, 2018, 4 (1): 42- 49.
13	LIU Z , GAO X G , FU X W . A cooperative search and coverage algorithm with control lable revisit and connectivity maintenance for multiple unmanned aerial vehicles[J]. Sensors, 2018, 18 (5): 1472- 1506. doi: 10.3390/s18051472
14	TAVASSOLIPOUR M , MOTAHARI S A , SHALMANI M T . Learning of Gaussian processes in distributed and communication limited systems[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2017, PP (99): 1- 14.
15	程贲, 谭跃进, 黄魏. 基于能力需求视角的武器装备体系评估[J]. 系统工程与电子技术, 2011, 33 (2): 320- 323.
	CHENG B , TAN Y J , HUANG W . Weapon system-of-systems evaluation based on capability requirement perspective[J]. Systems Engineering and Electronics, 2011, 33 (2): 320- 323.
16	ZHAO Q S , LI S F , DOU Y J , et al. An approach for weapon system-of-systems scheme generation based on a super net work granular analysis[J]. IEEE Systems Journal, 2015, 11 (4): 1971- 1982.
17	DANELLJAN M, SHAT G, KHAN F S, et al. Efficient convolution operators for tracking[C]//Proc.of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6931-6939.
18	昝翔, 陈春良, 张仕新. 考虑权重演化的装备重要度动态评估方法[J]. 系统工程与电子技术, 2017, 39 (9): 2022- 2030.
	CHU X , CHEN C L , ZHANG S X . Dynamic evaluation method for equipment important degree considering weight-evolving[J]. Systems Engineering and Electronics, 2017, 39 (9): 2022- 2030.
19	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-01-16]. https://arxiv.org/abs/1409.1556.
20	YOO S, YUN K, CHOI J Y, et al. Action-decision networks for visual tracking with deep reinforcement learning[C]//Proc.of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1349-1358.
21	刘彦, 陈春良, 昝翔. 考虑双层耦合复杂网络的装备重要度评估方法[J]. 兵工学报, 2018, 39 (9): 1829- 1840.
	LIU Y , CHEN C L , CHU X . Evaluation method for equipment importance considering Bi-layer coupled complex network[J]. Acta ArmamentarⅡ, 2018, 39 (9): 1829- 1840.
22	VERBERT K A J , DE SCHUTTER B H K , BABUSKA R . Timely condition-based maintenance planning for multi-component systems[J]. Reliability Engineering & System Safety, 2017, 159 (3): 310- 321.
23	ALASWAD S , CASSADY R , POHL E A , et al. A model of system limiting availability under imperfect maintenance[J]. Journal of Quality in Maintenance Engineering, 2017, 23 (4): 415- 436. doi: 10.1108/JQME-06-2016-0024
24	李锴, 吴纬, 刘福胜. 基于PageRank算法的武器装备体系重要节点评估[J]. 火力与指挥控制, 2017, 42 (11): 34- 37.
	LI K , WU W , LIU F S . Evaluating nodes importance in equipment system-of-systems based on PageRank algorithm[J]. Fire Control & Command Control, 2017, 42 (11): 34- 37.
25	NGUYEN K A , DO P , GRALL A . Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum's structural importance[J]. Reliability Engineering & System Safety, 2017, 168 (10): 249- 261.
26	LIN Z L , HUANG Y S , FANG C C . Non-periodic preventive maintenance with reliability the thresholds for complex repairable systems[J]. Reliability Engineering & System Safety, 2015, 136 (4): 145- 156.
27	CHEN Z , XIA T B , PAN E S . Optimal multi-level classification and preventive maintenance policy for highly reliable products[J]. International Journal of Production Research, 2017, 55 (8): 2232- 2250. doi: 10.1080/00207543.2016.1232497
28	DOYEN L , GAUDOIN O , SYAMSUNDAR A . On geometric reduction of age or intensity models for imperfect maintenance[J]. Reliability Engineering & System Safety, 2017, 168 (10): 40- 52.
29	GUI G , SHENG U , HE J , et al. Ship detection using compact polarimetric SAR based on the notch filter[J]. IEEE Trans.on Geoscience & Remote Sensing, 2018, 56 (9): 5380- 5393.
30	JIAO J , ZHANG Y , SUN H , et al. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection[J]. IEEE Access, 2018, 6 (1): 20881- 20892.
31	李尔玉, 龚建兴, 黄健. 基于功能链的融合网络功能抗毁性评估[J]. 兵工学报, 2019, 40 (7): 1450- 1459.
	LI E Y , GONG J X , HUANG J . Analysis about functional invulnerability of convergent network based on function chain[J]. Acta ArmamentarⅡ, 2019, 40 (7): 1450- 1459.
32	ZHANG X, ZOU Y, SHI W. Dilated convolution neural network with leaky ReLU for environmental sound classification[C]//Proc.of the IEEE 22nd International Conference on Digital Signal Processing, 2017.
33	KIM J H , KIM W C , KWON D G , et al. Robust equity portfolio performance[J]. Annals of Operations Research, 2018, 266 (2): 1- 20.
34	马纪, 刘希喆. 基于序关系-熵权法的低压配网台区健康状态评估[J]. 电力系统保护与控制, 2017, 45 (6): 87- 93.
	MA J , LIU X Z . Evaluation of health status of low-voltage distribution network based on order relation-entropy weight method[J]. Power System Protection and Control, 2017, 45 (6): 87- 93.
35	DRAUDVILIENE I , MESKUOTIENE A , RAISUTIS R , et al. The capability assessment of the spectrum decomposition technique for measurements of the group velocity of lamb waves[J]. Journal of Nondestructive Evaluation, 2018, 37 (2): 29- 42. doi: 10.1007/s10921-018-0484-2
36	DEB K , JAIN H . An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE Trans.on Evolutionary Computation, 2014, 18 (4): 577- 601. doi: 10.1109/TEVC.2013.2281535
37	MAHAJAN A , DAFNOMILIS I , HANCOCK V , et al. Assensing the representativeness of durability tests for wood pellets by DEM simulation-comparing conditions in a durability test with transfer chutes[J]. The European Physical Journal Conferences, 2017, 140, ID 15004. doi: 10.1051/epjconf/201714015004

目标名称	目标分类	T₁	T₂	T₃	T₄
W防空导弹阵地	火力打击目标	9	9	4	4
B防空导弹阵地	火力打击目标	9	9	4	4
E防空导弹阵地	火力打击目标	7	9	4	4
F防空导弹阵地	火力打击目标	7	9	4	4
H防空导弹阵地	火力打击目标	9	9	4	4
I防空导弹阵地	火力打击目标	7	9	4	4
K防空导弹“天龙”阵地	火力打击目标	4	9	4	5
N防空导弹阵地	火力打击目标	5	9	4	4
R防空导弹阵地	火力打击目标	9	9	4	4
S防空导弹阵地	火力打击目标	9	9	4	4
Z空军基地	火力打击目标	8	9	7	1
第1-1飞弹营1连	火力打击目标	3	9	3	6
第1-1飞弹营2连	火力打击目标	3	9	3	6
第3中队	火力打击目标	1	9	3	8
C南部远程预警雷达站	侦察情报目标	9	6	2	4
L远程预警雷达站	侦察情报目标	9	6	2	4
J防卫指挥部	指挥控制目标	8	5	8	2
M防卫指挥部	指挥控制目标	8	5	8	2
O防卫指挥部	指挥控制目标	6	5	8	2
P防卫指挥部	指挥控制目标	6	5	8	2
参谋本部	指挥控制目标	9	5	9	1
国防部	指挥控制目标	6	5	8	1
海军A特遣部队指挥中心	指挥控制目标	5	5	8	3
空军作战指挥部	指挥控制目标	7	5	8	2
陆军第1-3军团指挥部	指挥控制目标	5	5	8	3
陆军第1-5军团指挥部	指挥控制目标	5	5	8	3

排名	熵权法		熵权法		熵权理想点法
排名	目标名称	评分	目标名称	评分	目标名称	评分
1	W防空导弹阵地	0.47	W防空导弹阵地	0.70	L远程预警雷达站	0.71
2	R防空导弹阵地	0.47	R防空导弹阵地	0.70	C南部远程预警雷达站	0.71
3	B防空导弹阵地	0.47	B防空导弹阵地	0.70	W防空导弹阵地	0.68
4	H防空导弹阵地	0.47	H防空导弹阵地	0.70	R防空导弹阵地	0.68
5	S防空导弹阵地	0.47	S防空导弹阵地	0.70	B防空导弹阵地	0.68
6	L远程预警雷达站	0.47	L远程预警雷达站	0.69	H防空导弹阵地	0.68
7	C南部远程预警雷达站	0.47	C南部远程预警雷达站	0.69	S防空导弹阵地	0.68
8	E防空导弹阵地	0.42	E防空导弹阵地	0.67	E防空导弹阵地	0.64
9	I防空导弹阵地	0.42	I防空导弹阵地	0.67	I防空导弹阵地	0.64
10	F防空导弹阵地	0.42	F防空导弹阵地	0.67	F防空导弹阵地	0.64

排名	目标名称	体系价值/%
第1名	空军作战指挥部	9.29
第2名	参谋本部	5.05
第3名	国防部	4.48
第4名	第3中队	4.08
第5名	LINK-16地面站1	3.98
第6名	W防空导弹阵地	3.98
第7名	第1-1飞弹营1连	3.91
第8名	第1-1飞弹营2连	3.91
第9名	N防空导弹阵地	3.88
第10名	K防空导弹“天龙”阵地	3.87

排名	目标1	目标2	体系价值/%
第1名	国防部	参谋本部	14.02
第2名	国防部	空军作战指挥部	13.53
第3名	国防部	P防卫指挥部	13.08
第4名	国防部	陆军第1-3军团指挥部	13.06
第5名	国防部	海军A特遣部队指挥中心	13.03
第6名	国防部	J防卫指挥部	13.00
第7名	国防部	M防卫指挥部	13.00
第8名	国防部	Z空军基地	12.94
第9名	国防部	O防卫指挥部	12.93
第10名	国防部	陆军第1-5军团指挥部	12.88

[1]	Tongliang LU, Wenhao CHEN, Bingfeng GE, Qiling DENG. Multi-layer network modeling for combat system-of-systems under information support [J]. Systems Engineering and Electronics, 2022, 44(2): 520-528.
[2]	Xin ZHOU, Weiping WANG, Yifan ZHU, Tao WANG, Tian JING. Unmanned equipment SoS architecture scheme space searchingmethod based on the sequential allocated mechanism [J]. Systems Engineering and Electronics, 2021, 43(11): 3211-3219.
[3]	WANG Shou-biao, LI Xin-ming, LIU Dong. Super-network model of architecture for weapon equipment system of systems based on granular computing [J]. Systems Engineering and Electronics, 2016, 38(4): 836-843.

Value evaluation of joint fire strike target based on system-of-systems attack

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 37

Related Articles 3

Recommended Articles

Metrics

Comments