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

Abstract

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

CLC Number:

O212.4
TP391

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.

Figures/Tables 8

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Fig.3

Table 1

Table 2

Table 3

Table 4

Table 5

References 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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[2]	Yingqi LU, Chengli FAN, Qiang FU, Xiaowen ZHU, Wei LI. Missile defense target threat assessment based on improved similarity measure and information entropy of IFRS [J]. Systems Engineering and Electronics, 2022, 44(4): 1230-1238.
[3]	Bowen YU, Lin YU, Ming LYU, Jie ZHANG. Target threat assessment model based on M-ANFIS-PNN [J]. Systems Engineering and Electronics, 2022, 44(10): 3155-3163.
[4]	Chong JIN, Juan SUN, Yongjia WANG, Pushen CAI, Xin RONG. Threat comprehensive assessment for air defense targets based on intuitionistic fuzzy TOPSIS and variable weight VIKOR [J]. Systems Engineering and Electronics, 2022, 44(1): 172-180.
[5]	Guanglei MENG, Mingzhe ZHOU, Haiyin PIAO, Huimin ZHANG. Threat assessment method of dual-aircraft formation based on cooperative tactical recognition [J]. Systems Engineering and Electronics, 2020, 42(10): 2285-2293.
[6]	SUN Haiwen, XIE Xiaofang, SUN Tao, ZHANG Longjie. Threat assessment method of warships formation air defense based on DBN under the condition of small sample data missing [J]. Systems Engineering and Electronics, 2019, 41(6): 1300-1308.
[7]	WANG Zhuo, ZHENG Xuehe, CHANG Xiaolan. High-speed stealth target detection algorithm based on rotating and dimension reduction [J]. Systems Engineering and Electronics, 2019, 41(1): 14-20.
[8]	SUN Qingpeng, LI Zhanwu, CHANG Yizhe. Multi-types airplane threat assessment based on combat power field [J]. Systems Engineering and Electronics, 2018, 40(9): 1993-1999.
[9]	XU Ximeng, YANG Rennong, FU Ying, ZHAO Yu. Target threat assessment in air combat based on ELM_AdaBoost strong predictor [J]. Systems Engineering and Electronics, 2018, 40(8): 1760-1768.
[10]	ZHANG Ying, WANG Hongwei, CHEN You. Combined emitter threat assessment based on ICWM-RCM [J]. Systems Engineering and Electronics, 2018, 40(3): 557-562.
[11]	OUYANG Zhihong, XUE Lei, DING Feng. Jamming target assignment method of regional electronic air defense against electrooptical precision guided weapon#br# [J]. Systems Engineering and Electronics, 2018, 40(12): 2621-2628.
[12]	SUN Haiwen, XIE Xiaofang, SUN Tao, ZHANG Longjie. Threat assessment method of warships formation air defense based on DDBN-cloud model [J]. Systems Engineering and Electronics, 2018, 40(11): 2466-.
[13]	ZHANG Haowei, XIE Junwei, GE Jiaang, ZHANG Zhaojian, ZONG Binfeng. Intuitionistic fuzzy set threat assessment based on improved TOPSIS and multiple times fusion#br# [J]. Systems Engineering and Electronics, 2018, 40(10): 2263-2269.
[14]	LI Zhengxin, ZHANG Fengming, WANG Ying, TAO Qian, LI Chao. Method of missing data imputation for multivariate time series [J]. Systems Engineering and Electronics, 2018, 40(1): 225-230.
[15]	TANG Xin, YANG Jianjun, FENG Song, REN Baoxiang. AR(p) dynamic catastrophe ranking method of target threat assessment under the loss of data [J]. Systems Engineering and Electronics, 2017, 39(5): 1058-1064.

Multi-time threat assessment based on dynamic grey principal component analysis

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指标	A			B			C			D
指标	1	2	3	1	2	3	1	2	3	1	2	3
航路捷径/km	130	200	80	40	80	40	100	110	80	230	100	70
攻击航向角/(°)	65	70	40	40	50	50	50	65	45	70	55	60
速度/(km/h)	200	180	25	25	80	35	50	55	60	150	105	180
阵位距离/km	80	150	60	35	20	35	40	45	30	50	20	25
武器数量	2	1	3	4	2	2	2	2	2	4	1	1
重访时间/min	3	2	3.5	2	3.5	4	3.5	2	3	3.5	2	3.5
分辨率/m	0.5	1	2	0.3	0.9	0.8	0.6	0.55	0.6	1	0.55	0.6
传输速率/(bit/s)	30	20	25	25	30	30	35	26	30	33	28	30
通信容量/(bit/s)	40	25	30	45	40	40	25	33	40	30	35	45
噪声/dB	10	15	50	30	20	20	45	42	35	50	35	48
探测距离/km	3	5	1	5	5	4	2	5	3	5	1	1
信噪比/dB	80	60	120	70	80	90	88	85	90	85	50	50
误码率/%	15	10	40	20	30	25	30	44	40	34	40	45