基于动态灰色主成分分析的多时刻威胁评估

doi:10.12305/j.issn.1001-506X.2021.03.18

摘要/Abstract

摘要：

针对目标威胁评估问题的高维数、时变性等特点, 提出基于动态灰色主成分分析(dynamic grey principal component analysis, DG-PCA)的多时刻威胁评估方法。首先, 以潜艇为分析对象, 构建协同作战模式下的威胁评估指标体系。其次, 提出扩展灰色相似关联度用于表征指标间动态非线性相关度, 构建用于多元非线性时间序列分析的DG-PCA模型。最后, 结合层次分析法(analytic hierarchy process, AHP)与时间序列赋权, 建立基于加权DG-PCA的多时刻威胁评估模型。分析结果表明, 所提方法能够融合多时刻信息, 符合作战实际, 并且在小样本情况下具有较好的数据提取能力。

关键词: 威胁评估, 多元时间序列, 灰色相似关联度, 主成分分析, 降维

Abstract:

Aiming at the high dimension and time-varying characteristics of target threat assessment, a multi-time threat assessment method based on dynamic grey principal component analysis (DG-PCA) is proposed. Firstly, the submarine is taken as the analysis object, and the threat assessment index system under the cooperative operation mode is constructed. Secondly, the extended grey similarity correlation degree is proposed to represent the dynamic nonlinear correlation among indexes, and the DG-PCA model for multivariate nonlinear time series analysis is constructed. Finally, combined with analytic hierarchy process (AHP) and time series weighting, a multi-time threat assessment model based on weighted DG-PCA is established. The analysis results show that the proposed method can fuse multi-time information, accord with the actual combat situation, and has good data extraction ability in the case of small samples.

Key words: threat assessment, multivariate time series, grey similarity incidence degree, principal component analysis(PCA), dimension reduction

中图分类号:

O212.4
TP391

孙云柯, 方志耕, 陈顶. 基于动态灰色主成分分析的多时刻威胁评估[J]. 系统工程与电子技术, 2021, 43(3): 740-746.

Yunke SUN, Zhigeng FANG, Ding CHEN. Multi-time threat assessment based on dynamic grey principal component analysis[J]. Systems Engineering and Electronics, 2021, 43(3): 740-746.

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参考文献 30

1	郭辉, 徐浩军, 刘凌. 基于区间数TOPSIS法的空战目标威胁评估[J]. 系统工程与电子技术, 2009, 31 (12): 2914- 2917. doi: 10.3321/j.issn:1001-506X.2009.12.026
	GUO H , XU H J , LIU L . Threat assessment for air combat target based on interval TOPSIS[J]. Systems Engineering and Electronics, 2009, 31 (12): 2914- 2917. doi: 10.3321/j.issn:1001-506X.2009.12.026
2	SAHNI M , DASS K . A method of risk analysis and threat management using AHP: an application to air defence[J]. Journal of Battlefield Technology, 2015, 18 (3): 27- 33.
3	SUN H W , XIE X F . Threat evaluation method of warships formation air defense based on AR(p)-DITOPSIS[J]. Journal of Systems Engineering and Electronics, 2019, 30 (2): 297- 307. doi: 10.21629/JSEE.2019.02.09
4	GAO Y , LI D S , ZHONG H . A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment[J]. Engineering Applications of Artificial Intelligence, 2020, 87 (1): 103276.
5	奚之飞, 徐安, 寇英信, 等. 基于改进GRA-TOPSIS的空战威胁评估[J]. 北京航空航天大学学报, 2020, 46 (2): 388- 397.
	XI Z F , XU A , KOU Y X , et al. Air combat threat assessment based on improved GRA-TOPSIS[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46 (2): 388- 397.
6	LIU B , XIA H B , LONG W B , et al. Threat degree sorted of multi-protected-target in air combat platform[J]. Advances in Computer, Signals and Systems, 2016, (1): 8- 12.
7	张浩为, 谢军伟, 葛佳昂, 等. 改进TOPSIS的多态融合直觉模糊威胁评估[J]. 系统工程与电子技术, 2018, 40 (10): 2263- 2269. doi: 10.3969/j.issn.1001-506X.2018.10.16
	ZHANG H W , XIE J W , GE J A , et al. Intuitionistic fuzzy set threat assessment based on improved TOPSIS and multiple times fusion[J]. Systems Engineering and Electronics, 2018, 40 (10): 2263- 2269. doi: 10.3969/j.issn.1001-506X.2018.10.16
8	XU X M, YANG R N. Threat assessment in air combat based on ELM neural network[C]//Proc.of the IEEE International Conference on Artificial Intelligence and Computer Applications, 2019: 114-120.
9	YUE L F , YANG R N , ZUO J L , et al. Air target threat assessment based on improved moth flame optimization-gray neural network model[J]. Mathematical Problems in Engineering, 2019, (8): 1- 14.
10	SUN H W , XIE X F , SUN T , et al. Dynamic Bayesian network threat assessment for warship formation: a data analysis method[J]. International Journal of High Performance Systems Architecture, 2018, 8 (12): 71- 81.
11	KUMAR S , TRIPATHI B K . Modeling of threat evaluation for dynamic targets using Bayesian network approach[J]. Procedia Technology, 2016, 24 (2): 1268- 1275.
12	KONG D P , CHANG T Q , WANG Q D , et al. A threat assessment method of group targets based on interval-valued intuitionistic fuzzy multi-attribute group decision-making[J]. Applied Soft Computing, 2018, 67, 350- 369. doi: 10.1016/j.asoc.2018.03.015
13	PARTRIDGE M , CALVO R A . Fast dimensionality reduction and simple PCA[J]. Intelligent data analysis, 1998, 2 (3): 203- 214. doi: 10.3233/IDA-1998-2304
14	YATA K , AOSHIMA M . Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations[J]. Journal of Multivariate Analysis, 2012, 105 (1): 193- 215. doi: 10.1016/j.jmva.2011.09.002
15	YANG D , SONG G B , WANG Z G , et al. Damage detection of refractory based on principle component analysis and Gaussian mixture model[J]. Complexity, 2018, (11): 115031.
16	YIN G Y , ZHOU S L , ZHANG W G . A threat assessment algorithm based on AHP and principal components analysis[J]. Procedia Engineering, 2011, 15, 4590- 4596. doi: 10.1016/j.proeng.2011.08.862
17	LIU X M, HAN Y, QIU H Z, et al. Threat evaluation in air defense based on Improved KPCA-TOPSIS[C]//Proc.of the IEEE Guidance, Navigation and Control Conference, 2018.
18	MENG F Z, FU Y S, LOU F. A network threat analysis method combined with kernel PCA and LSTM-RNN[C]//Proc.of the 10th International Conference on Advanced Computational Intelligence, 2018: 508-513.
19	董雪, 张德平. 基于组合核主成分分析的潜艇威胁度评估模型[J]. 计算机工程, 2018, 44 (11): 46- 51. doi: 10.3778/j.issn.1002-8331.1710-0263
	DONG X , ZHANG D P . Submarine threat assessment model based on combined core principal component analysis[J]. Computer Engineering, 2018, 44 (11): 46- 51. doi: 10.3778/j.issn.1002-8331.1710-0263
20	JIANG Q C , YAN X F . Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 136 (15): 121- 137.
21	WANG Y W , ZHOU D H , CHEN M Y , et al. Weighted part mutual information related component analysis for quality-related process monitoring[J]. Journal of Process Control, 2020, 88, 111- 123. doi: 10.1016/j.jprocont.2020.03.001
22	袁周, 方志耕. 灰色主成分评价模型的构建及其应用[J]. 系统工程理论与实践, 2016, 36 (8): 2086- 2090.
	YUAN Z , FANG Z G . Construction and application of grey principal component evaluation model[J]. Systems Engineering-Theory & Practice, 2016, 36 (8): 2086- 2090.
23	王玲玲, 方志耕. 分层构权灰色主成分评价模型及其应用[J]. 控制与决策, 2019, 34 (6): 1300- 1306.
	WANG L L , FANG Z G . Multi-layer weighted grey principal component evaluation model and its application[J]. Control and Decision, 2019, 34 (6): 1300- 1306.
24	李正欣, 郭建胜, 惠晓滨, 等. 基于共同主成分的多元时间序列降维方法[J]. 控制与决策, 2013, 28 (4): 54- 59.
	LI Z X , GUO J S , HUI X B , et al. Dimension reduction method for multivariate time series based on common principal component[J]. Control and Decision, 2013, 28 (4): 54- 59.
25	VITANOV N K , HSFFMANN N P , WERNITZ B . Nonlinear time series analysis of vibration data from a friction brake: SSA, PCA, and MFDFA[J]. Chaos, Solitons & Fractals, 2014, 69, 90- 99.
26	DONG Y N , QIN S J . A novel dynamic PCA algorithm for dynamic data modeling and process monitoring[J]. Journal of Process Control, 2018, 67, 1- 11. doi: 10.1016/j.jprocont.2017.05.002
27	SREEDHARAN J , JEE V A . Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA[J]. Journal of Mechanical Engineers, 2020, 234 (5): 1014- 1030.
28	LIU S F , XIE N M , FORREST J . Novel models of grey relational analysis based on visual angle of similarity and nearness[J]. Grey Systems: Theory and Application, 2011, 1 (1): 8- 18. doi: 10.1108/20439371111106696
29	刘思峰, 杨英杰, 吴利丰. 灰色系统理论及其应用[M]. 北京: 科学出版社, 2014.
	LIU S F , YANG Y J , WU L F . Grey system theory and its application[M]. Beijing: Science Press, 2014.
30	周大镯, 姜文波, 李敏强. 一个高效的多变量时间序列聚类算法[J]. 计算机工程与应用, 2010, 46 (1): 141- 143.
	ZHOU D Z , JIANG W B , LI M Q . An efficient clustering algorithm for multivariate time series[J]. Computer Engineering & Applications, 2010, 46 (1): 141- 143.

	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂	X₁₃
X₁	1	0.809	0.769	0.559	0.535	0.518	0.607	0.553	0.654	0.698	0.557	0.66	0.722
X₂	0.809	1	0.817	0.530	0.570	0.530	0.719	0.602	0.696	0.596	0.581	0.701	0.698
X₃	0.769	0.817	1	0.542	0.564	0.533	0.694	0.596	0.725	0.610	0.696	0.615	0.610
X₄	0.559	0.530	0.542	1	0.585	0.710	0.595	0.729	0.548	0.573	0.611	0.763	0.604
X₅	0.535	0.570	0.564	0.585	1	0.687	0.814	0.765	0.603	0.546	0.533	0.598	0.596
X₆	0.518	0.530	0.533	0.710	0.687	1	0.617	0.803	0.538	0.517	0.421	0.668	0.573
X₇	0.607	0.719	0.694	0.595	0.814	0.617	1	0.695	0.723	0.604	0.535	0.718	0.546
X₈	0.553	0.602	0.596	0.729	0.765	0.803	0.695	1	0.594	0.544	0.526	0.653	0.544
X₉	0.654	0.696	0.725	0.548	0.603	0.538	0.723	0.594	1	0.722	0.897	0.632	0.517
X₁₀	0.698	0.596	0.610	0.573	0.546	0.517	0.604	0.544	0.722	1	0.857	0.544	0.874
X₁₁	0.557	0.581	0.696	0.611	0.533	0.421	0.535	0.526	0.897	0.857	1	0.517	0.423
X₁₂	0.66	0.701	0.615	0.763	0.598	0.668	0.718	0.653	0.632	0.544	0.517	1	0.471
X₁₃	0.722	0.698	0.610	0.604	0.596	0.573	0.546	0.544	0.517	0.874	0.423	0.471	1

主成分	特征值	方差贡献率/%	累计方差贡献率/%
1	8.551 3	64.59	64.59
2	1.148 6	8.67	73.26
3	0.795 7	6.01	79.27
4	0.725 8	5.48	84.95
5	0.573 3	4.33	89.08
6	0.361 2	2.73	91.81
7	0.248 5	1.88	93.69
8	0.203 1	1.53	95.22
9	0.177 4	1.34	96.56
10	0.141 3	1.07	97.63
11	0.126 5	0.96	98.59
12	0.120 0	0.91	99.49
13	0.067 2	0.51	100.00

威胁	加权得分	排序	未加权得分	排序
A	0.116 9	2	136.139	2
B	0.113 2	3	148.937	1
C	0.118 5	1	104.320	3
D	0.102 8	4	72.078	4

主成分	DG-PCA			PCA			组合KPCA
主成分	特征值	方差贡献率/%	累计方差贡献率/%	特征值	方差贡献率/%	累计方差贡献率/%	特征值	方差贡献率/%	累计方差贡献率/%
1	8.551 3	64.59	64.59	5.030 7	51.98	51.98	8.227 0	61.97	61.97
2	1.148 6	8.67	73.26	1.575 3	16.28	68.26	1.094 3	8.24	70.22
3	0.795 7	6.01	79.27	0.623 2	6.44	74.69	1.494 7	11.26	81.48
4	0.725 8	5.48	84.75	0.540 6	5.59	80.28	1.240 6	9.35	90.82
5	0.573 3	4.33	89.08	0.503 7	5.20	85.48	0.943 0	7.10	97.92

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