基于生成对抗网络的防空体系态势辅助分析

doi:10.12305/j.issn.1001-506X.2022.08.17

Abstract

Abstract:

Aiming at the lack of system perspective of the current situation analysis model for air defense system and the poor interpretability for commanders to analyze the results, this paper proposes an auxiliary situation analysis model based on generative adversarial network for air defense system. Firstly, the situational information and combat capability of the air defense system are described in a graphical method so that it is easily to interpret the result to human commanders. Secondly, using generative adversarial network to simulate the process of human commanders' situation analysis, which can obtain the capability map of air defense system from superficial situation feature. Finally, using multiple indicators to compare the results of various models. The experiment results show that the model proposed in this paper can analyze from the system perspective, and obtain the capability map of the air defense system. The accuracy of the generated image is at least 34.1% higher than other models.

Key words: auxiliary situation analysis, air defense system, generative adversarial network (GAN)

CLC Number:

TP183

Rongxiang LIU, Lin WU, Zhige XIE, Honglin LIU. Auxiliary situation analysis for air defense system based on generative adversarial network[J]. Systems Engineering and Electronics, 2022, 44(8): 2522-2529.

Figures/Tables 13

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References 30

1	BABER C , VANCE C . Supporting decision making in a simulated air defence activity[J]. Contemporary Ergonomics & Human Factors, 2020,
2	葛顺, 夏学知. 用于战术意图识别的动态序列贝叶斯网络[J]. 系统工程与电子技术, 2014, 36 (1): 76- 83.
	GE S , XIA X Z . DSBN used for recognition of tactical intention[J]. Systems Engineering and Electronics, 2014, 36 (1): 76- 83.
3	WRIGHT E, MAHONEY S, LASKEY K B, et al. Multi-entity Bayesian networks for situation assessment[C]//Proc. of the International Conference on Information Fusion, 2002: 804-811.
4	CAO L, ZHANG A, WANG Q, et al. Research on situation assessment of UCAV based on dynamic Bayesian networks in complex environment[C]//Proc. of the International Conference on Intelligent Computing for Sustainable Energy and Environment, 2010: 58-68.
5	尹翔, 张萌, 陈梦乔. 基于判别分析的空中目标作战意图识别[J]. 弹箭与制导学报, 2018, 38 (3): 46- 50.
	YIN X , ZHANG M , CHEN M Q . Combat intention recognition of the target in the air based on discriminant analysis[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2018, 38 (3): 46- 50.
6	ZHANG W X, HILL R W. A template-based and pattern-driven approach to situation awareness and assessment in virtual humans[C]// Proc. of the 4th International Conference on Autonomous Agents, 2000: 116-123.
7	岳师光. 面向计算机生成兵力的意图识别建模与推理方法研究[D]. 长沙: 国防科学技术大学, 2016.
	YUE S G. Research on modeling and inference of CGF-oriented intention recognition[D]. Changsha: National University of Defense Technology, 2016.
8	张振兴, 杨任农, 房育寰, 等. 基于IL-HMM的目标机动动作识别[J]. 飞行力学, 2018, 36 (5): 92- 96.
	ZHANG Z X , YANG R N , FANG Y H , et al. Target maneuver recognition based on IL－HMM model[J]. Flight Dynamics, 2018, 36 (5): 92- 96.
9	陶九阳, 吴琳, 王驰, 等. 基于深度学习的战场态势变化速度预测模型[J]. 系统仿真学报, 2018, 30 (3): 785- 792.
	TAO J Y , WU L , WANG C , et al. A model for battlefield situation change rate prediction based on deep learning[J]. Journal of System Simulation, 2018, 30 (3): 785- 792.
10	朱丰, 胡晓峰, 贺筱媛, 等. 一种基于CNN的样本不足战场包围态势认知方法[J]. 系统仿真学报, 2017, 29 (10): 2291- 2300.
	ZHU F , HU X F , HE X Y , et al. A CNN based cognitive method to battlefields encompassing situation with insufficient samples[J]. Journal of System Simulation, 2017, 29 (10): 2291- 2300.
11	欧微, 柳少军, 贺筱媛, 等. 战场对敌目标战术意图智能识别模型研究[J]. 计算机仿真, 2017, 34 (9): 10- 14.10-14, 19 doi: 10.3969/j.issn.1006-9348.2017.09.003
	OU W , LIU S J , HE X Y , et al. Study on the intelligent re-cognition on model of enemy targets tactical intention on battlefield[J]. Computer Simulation, 2017, 34 (9): 10- 14.10-14, 19 doi: 10.3969/j.issn.1006-9348.2017.09.003
12	姚庆锴, 柳少军, 贺筱媛, 等. 基于深度学习的空中任务识别方法研究[J]. 系统仿真学报, 2017, 29 (9): 2227- 2231.
	YAO Q K , LIU S J , HE X Y , et al. Research of air mission recognition method based on deep learning[J]. Journal of System Simulation, 2017, 29 (9): 2227- 2231.
13	郭圣明, 贺筱媛, 吴琳, 等. 基于强制稀疏自编码神经网络的作战态势评估方法研究[J]. 系统仿真学报, 2018, 30 (3): 772- 784.772-784, 800
	GUO S M , HE X Y , WU L , et al. Situation assessment approach for air defense operation system based on force-sparsed stacked-auto encoding neural networks[J]. Journal of System Simulation, 2018, 30 (3): 772- 784.772-784, 800
14	姚晓毅, 郭圣明, 胡晓峰, 等. 基于MINE-FNA组合算法的防空体系指标网构建方法[J]. 指挥控制与仿真, 2016, 38 (1): 94- 100. doi: 10.3969/j.issn.1673-3819.2016.01.020
	YAO X Y , GUO S M , HU X F , et al. Indices network construction for air defense system of system based on MINE-FNA assembled algorithm[J]. Command Control & Simulation, 2016, 38 (1): 94- 100. doi: 10.3969/j.issn.1673-3819.2016.01.020
15	KIM J . A comparison of guided missile simulations between EADSIM and SADM in composite combat mission planning simulation environments[J]. Journal of Korea Multimedia Society, 2020, 23 (8): 1066- 1074.
16	GOODFELLOW I J , POUGET-ABADIE J , MIRZA M , et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014, 3, 2672- 2680.
17	RADFORD A, METZ L, CHINTALA S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[C]//Proc. of the International Conference on Learning Representations, 2016.
18	KARRAS T, AILA T, LAINE S, et al. Progressive growing of GANs for improved quality, stability, and variation[C]//Proc. of the International Conference on Learning representations, 2018.
19	HWANG J , AZERNIKOV S , EFROS A A , et al. Learning beyond human expertise with generative models for dental restorations[J]. Computer Vision and Pattern Recognition, 2018,
20	ISOLA P , ZHU J , ZHOU T , et al. Image-to-image translation with conditional adversarial networks[J]. Computer Vision and Pattern Recognition, 2017, 5967- 5976.
21	LIU M , BREUEL T M , KAUTZ J , et al. Unsupervised image-to-image translation networks[J]. Neural Information Processing Systems, 2017, 700- 708.
22	WANG T C , LIU M Y , ZHU J Y , et al. High-resolution image synthesis and semantic manipulation with conditional GANs[J]. Computer Vision and Pattern Recognition, 2018, 8798- 8807.
23	REED S E , AKATA Z , MOHAN S , et al. Learning what and where to draw[J]. Neural Information Processing Systems, 2016, 217- 225.
24	NGUYEN A T , CLUNE J , BENGIO Y , et al. Plug & play generative networks: conditional iterative generation of images in latent space[J]. Computer Vision and Pattern Recognition, 2017, 3510- 3520.
25	TAN F W, FENG S, ORDONEZ V, et al. Text2Scene: generating compositional scenes from Textul descriptions[C]// Proc. of the IEEE Computer Vision and Pattern Recognition, 2020.
26	YARLAGADDA S K , GVERA D , BESTAGINI P , et al. Satellite image forgery detection and localization using gan and one-class classifier[J]. Electronic Imaging, 2018, 2018 (7): 2141- 2149.
27	CHUNG Y A, WU C C, SHEN C H, et al. Unsupervised learning of audio segment representations using sequence-to-sequence recurrent neural networks[C]//Proc. of the Interspeech, 2016: 765-769.
28	孔德江, 汤斯亮, 吴飞. 时空嵌入式生成对抗网络的地点预测方法[J]. 模式识别与人工智能, 2018, 31 (1): 49- 60.
	KONG D J , TANG S L , WU F . Location prediction method for space-time embedded generation of antagonistic network[J]. Pattern Recognition and Artificial Intelligence, 2018, 31 (1): 49- 60.
29	殷小静, 胡晓峰, 刘戎翔, 等. 基于GAN的体系能力图谱生成方法[J]. 系统工程与电子技术, 2020, 42 (10): 2257- 2264. doi: 10.3969/j.issn.1001-506X.2020.10.14
	YIN X J , HU X F , LIU R X , et al. Generative method of the SOS capability maps based on GAN[J]. Systems Engineering and Electronics, 2020, 42 (10): 2257- 2264. doi: 10.3969/j.issn.1001-506X.2020.10.14
30	RONNEBERGER O , FISCHER P , BROX T , et al. U-Net: convolutional networks for biomedical image segmentation[J]. Medical Image Computing and Computer Assisted Intervention, 2015, 234- 241.

模型名称	指标
模型名称	NRMSE	PSNR	SSIM
V+MLP	0.156 46	5.745 32	0.136 70
G+MLP	0.100 33	6.315 79	0.180 25
GAN+L1	0.026 85	8.731 06	0.301 17

α₁	α₂	指标
α₁	α₂	NRMSE	PSNR	SSIM
1	0	0.258 08	2.047 86	0.099 33
0	100	0.025 81	8.837 77	0.306 73
1	100	0.030 47	8.322 69	0.296 96
1	1 000	0.026 85	8.731 06	0.301 17
1	10 000	0.027 02	8.699 68	0.303 06

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Auxiliary situation analysis for air defense system based on generative adversarial network

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