Screening and Evaluation of Operational Effectiveness Indicators for Air Defense Missiles Based on Improved PCA
Peng Zhang
Ke Feng
Jiancheng Gong
Congcong Gong
Kai Zhao
DOI: https://doi.org/10.59429/esta.v10i6.1687
Keywords: Improved Principal Component Analysis Method; Air Defense Missiles; MATLAB; Indicator Screening; Operational Effective_x005fness Evaluation
Abstract
With the significant role played by air defense missile weapon systems in various local wars and armed conflicts, the significance of operational effectiveness evaluation has become increasingly important. This article determines the operational efficiency index system model of the air defense missile weapon system by constructing the operational environment of the air defense missile weapon system. The improved principal component analysis(PCA) method and MATLAB programming are applied to reduce the dimensionality and decorrelation of the operational performance indicators of the air defense missile weapon system’s operational environment, and the optimized indi_x005fcator system model of the operational efficiency indicators of the air defense missile weapon system is output. At the same time, the weight value is also closer to the actual combat situation, to achieve an accurate and express evaluation of combat effectiveness. By analyzing a case of evaluating the operational effectiveness of a certain type of air defense missile weapon system, key indicators were selected, and the effectiveness of the model in screening the operational effectiveness evaluation indicators of air defense missile weapon systems was verified. This provides a basis or reference for the subsequent optimization of air defense missile weapon system schemes and the evaluation of operational effectiveness and has certain promoting significance.
References
[1] Guo Xiaochuan, Liu Xinchang, Chen Guiming, et al. Missile weapon system-of-systems optimization method based on information entropy[C]//2016 International Conference on Computer, Information and Telecommunication Systems (CITS): IEEE, 2016: 1-5.
[2] Ji Hongquan, Hou Qingsen, Wu Dehao. Modified performance-enhanced PCA for incipient fault detection of dynamic industrial
processes[J]. Journal of Process Control, 2023, 131: 103107.
[3] Ma Qingyue. Research on Comprehensive Evaluation Techniques for Weapon System of Systems’ Operational Effectiveness[D].
Harbin Institute of Technology, 2015
[4] Qiao Rong, Zhou Feng. Combat Effectiveness Evaluation of Surface-to-Air Missile Weapon Systems Based on PCA-BP Neural
Networks[J]. Military Operations Research and Systems Engineering, 2020, 34(04): 38-43, 67.
[5] Zhao Danling, Tan Yuejin, Li Jichao, et al. Armament System of Systems Contribution Evaluation Based on Operation Loop[J].
Systems Engineering and Electronics, 2017, 39(10): 2239-2247.
[6] Ozgur Tuncer, Hakan Ali Cirpan. Adaptive fuzzy based threat evaluation method for air and missile defense systems[J]. Information Sciences, 2023, 643: 119191.
[7] Wan Zhongyun, Li Yonggang, Zhang Zhizhong. Modeling and Analysis of Integrated Combat Network System Based on VOO_x005fDAC[C]//Proceedings of the International Conference on Industrial Control Network and System Engineering Research: ACM, 2019.
[8] Han Qi, Pang Bo, Li Sen, et al. Evaluation method and optimization strategies of resilience for air & space defense system of systems based on kill network theory and improved self-information quantity[J]. Defence Technology, 2023, 21(03): 219-239.
[9] Xiong Peisen, Liu Hu, Tian Yongliang. Mission Effectiveness Evaluation of Manned/Unmanned Aerial Team based on OODA and
Agent-Based Simulation[C]//Proceedings of 2019 3rd International Conference on Artificial Intelligence and Virtual Reality (AIVR 2019):
ACM, 2019: 14-20.
[10]Yang Weisheng, Wang Yu, Yang Yang, et al. Combat Network Effectiveness Evaluation Under Different Node Attack Strategies
Based on Operation Loop[J]. Engineering and Electronics, 2021, 43(11): 3220-3228.
[11]Danling ZHAO, Yajie DOU, Qingsong ZHAO, et al. The method to evaluate the command and control effectiveness of operational
system under uncertain threat situation[C]// 28th Chinese Control and Decision Conference(CCDC), 2016: 787-792.
[12]Quentin Voortman, Alexander Yu. Pogromsky, Alexey S. Matveev, et al. Consensus in networks of dynamical systems with limited
communication capacity[J]. Automatica, 2022, 145: 110514.
[13]Wang Jun, Zhaojie, Shao Lei, et al. System Effectiveness Evaluation Model of Ground to Air MissileBased on ADC Method[J].
Modern Defence Technology, 2015, 43(06): 13-20.
[14]Wang Jun, Zhao Jie, Li Jiong, et al. Research on numerical model of ground-to-air missile kill zone[J]. Systems Engineering Society of China, 2014, 34(12): 3260-3267.
[15] Zhi Hongxin, Zhao Peng, Li Zhong, et al. A Weapon-target Assignment in Air-defense Operations Based on Shooting Probability
Constraint[J]. Acta Arnamentarh, 2022, 43(04): 952-959.
[16]Qi Zhangxing, Chen Yewei, Liu Yuan,等. Radar signal recognition based on deep convolutional neural network in complex electromagnetic environment[C]//2022 3rd China International SAR Symposium (CISS): IEEE, 2022: 1-5.
[17]Hong-hao Zuo, Xiao-ming Li. A novel calculation method of radar jamming cover area for ground to air radar countermeasures[Z],
2016.
[18]Peibei Ma, Jun Ji, Jiangbo Sui, et al. Tactic Technical Performance Analysis of Missile Based on Set Pair Method[C]//2021 International Conference on Computer Engineering and Application (ICCEA): IEEE, 2021.
[19]Zhao Yueqiang, An Shi, Mai Qiang, et al. Effectiveness Modeling of Air Defense Missile Weapon System Based on ADC
Method[J]. Systems Engineering and Electronics, 2020, 42(09): 2003-2012.
[20]Gu Hui, Jiang Demao, Wang Guangyi, et al. Research on Effectiveness Evaluation Simulation Method of Surface-to-Air Missile
Weapon Systems[C]//Proceedings of the 2014 International Conference on Electrical,Control and Automation, 2014: 349-357.
[21]Wang Jun, Zhou Lin, Lei Humin, et al. Medium and Far Range Ground-to-Air Missile System Effectiveness Evaluation Model[J].
Journal of System Simulation, 2010, 22(07): 1761-1768, 1772. [22]Aosong Liang, Yunpeng Hu, Guannan Li. The impact of improved PCA
method based on anomaly detection on chiller sensor fault detection[J]. International Journal of Refrigeration, 2023, 155: 184-194.
[22]Dong Yuan, Nicholas Mancuso. SuSiE PCA: A Scalable Bayesian Variable Selection Technique for Principal Component Analysis[J]. Iscience, 2023: 108181.
[23]Yunsong Li, Jiahui Qu, Wenqian Dong, et al. Hyperspectral pansharpening via improved PCA approach and optimal weighted fusion strategy[J]. Neurocomputing, 2018, 315: 371-380.
[24]Kamila Zdybał, Elizabeth Armstrong, Alessandro Parente, et al. PCA fold: Python software to generate, analyze and improve
PCA-derived low-dimensional manifolds[J]. Softwarex, 2020, 12: 100630.
[25]Amir Eshaghi Chaleshtori, Abdollah Aghaie. A novel bearing fault diagnosis approach using the Gaussian mixture model and the
weighted principal component analysis[J]. Reliability Engineering & System Safety, 2024, 242: 109720.
[26] Fan junwei. The research of data extraction for remote sensing image information of NetCDF based on GDAL[D]. East China Institute of Technology, 20133
[27]Liu Yaosen, Zhang Zhouwei, Wang Yahong, et al. Overview of the Development of Russia’s Missile Defense System[J]. Aerospace
China, 2022, (06): 27-31.
[28]Bin Huang, Bong-Hwan Koh, Heung Soo Kim. PCA-based damage classification of delaminated smart composite structures using
improved layer wise theory[J]. Computers & Structures, 2014, 141: 26-35.
[29]Libiao Bai, Chaopeng Song, Xinyu Zhou, et al. Assessing project portfolio risk via an enhanced GA-BPNN combined with
PCA[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 106779.