UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

doi:10.23919/JSEE.2023.000062

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1235-1251.doi: 10.23919/JSEE.2023.000062

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UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

Lei XIE¹(), Shangqin TANG¹(), Zhenglei WEI²^,*(), Yongbo XUAN³(), Xiaofei WANG³()

¹ Institute of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China
² China Aerodynamics Research & Development Center, Mianyang 621000, China
³ Blue Sky Innovation Center for Frontier Science, Beijing 100000, China

Received:2021-02-24 Online:2023-10-18 Published:2023-10-30
Contact: Zhenglei WEI E-mail:310370487@qq.com;630909448@qq.com;zhenglei_wei@126.com;398791736@qq.com;wxf825421673@163.com
About author:
XIE Lei was born in 1997. He received his bachelor degree in arms engineering and master degree in armament science and technology from Air Force Engineering University, Xi’an, China. He is currently pursuing his doctor degree in Air Force Engineering University, Xi’an, Shaanxi, China. His research interests include air combat, intelligent optimization algorithm, maneuvering decision, and situation assessment. E-mail: 310370487@qq.com

TANG Shangqin was born in 1984. He received his bachelor and Ph.D. degrees in arms engineering from Air Force Engineering University in 2007 and 2013, respectively. His research interests include air combat maneuver decision, intention recognition, and situation assessment.E-mail: 630909448@qq.com

WEI Zhenglei was born in 1991. He received his B.S., M.S., and Ph.D. degrees in armament science and technology from Air Force Engineering University. He is an engineer in China Aerodynamics Research & Development Center. His research interests include maneuvering prediction and maneuvering decision. E-mail: zhenglei_wei@126.com

XUAN Yongbo was born in 1984. He received his Ph.D. degree from Air Force Engineering University in 2012. He is an engineer in Beijing Blue Sky Innovation Center for Frontier Science. His research interest is general technology. E-mail: 398791736@qq.com

WANG Xiaofei was born in 1990. He received his Ph.D. degree of weapon science and technology from Air Force Engineering University in 2020. He is an engineer in Beijing Blue Sky Innovation Center for Frontier Science. His research interests are evolutionary algorithms and UCAV air combat decision. E-mail: wxf825421673@163.com
Supported by:
This work was supported by the Natural Science Foundation of Shaanxi Province (2020JQ-481;2021JM-224), and the Aeronautical Science Foundation of China (201951096002).

Abstract

Abstract:

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

Key words: unmanned combat aerial vehicle (UCAV), situation assessment, clustering, K-means, stacked autoencoder, learning vector quantization

Lei XIE, Shangqin TANG, Zhenglei WEI, Yongbo XUAN, Xiaofei WANG. UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1235-1251.

Figures/Tables 26

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Table 1

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Table 2

Cluster evaluation index"

Index	Formula	Description
Ac	$A = \displaystyle \frac{ { {N_r} } }{N} \times 100$	$ {N_r} $ is the number of correct sample classifications; $ N $ is the total number of samples; the larger the Ac, the better the clustering effect.
Sil	${\rm{SI}} = \displaystyle \frac{1}{N}\displaystyle \sum_{i = 1}^N {\frac{ { {b_i} - {a_i} } }{ {\max ({b_i},{a_i})} } }$ , ${a_i} = \displaystyle \frac{1}{ { {N_k} } }\displaystyle \sum_{ {x_j} \in {c_k} } {{\rm{dist}}} ({x_i},{x_j})$ ${b_i} = {\mathop { {\rm{min} } }\limits_{h \in \left\{ {1,\cdots,k} \right\},h \ne k{\rm{ } } } }(\displaystyle \frac{1}{ { {N_k} } }\displaystyle \sum_{ {x_j} \in {c_k} } { {\rm{dist} } } ({x_i},{x_j}))$	$ a $ represents the average distance from the sample to other samples of the same category, $ b $ represents the shortest average distance from the sample to other samples of different categories, $ {\rm{SI}} \in [ - 1,1] $ , the larger the $ {\rm{SI}} $ , the better the clustering effect.
DB	${\rm{DB} } = \dfrac{1}{k}\displaystyle \sum_{j = 1}^k {\max \frac{ {s({c_k}) + s({c_r})} }{ { {\rm{dist} }({c_k},{c_l} )} }}$ $s({c_k}) = \displaystyle \frac{1}{n}\displaystyle \sum_{i = 1}^n {d({x_i},{c_k})}$	DB represents the proportion of cluster scatter between cluster separation.
CH	${\rm{CH} }(k) = \displaystyle \frac{ { {\rm{BGSS} } } }{ { {\rm{WGSS} } } } \times \frac{ {n - k} }{ {k - 1} }$ ${\rm{WGSS} } = \displaystyle \frac{1}{2}\displaystyle \sum_{i = 1}^k {({n_i} - 1)\overline d_i^2 }$ ${\rm{BGSS}} = \displaystyle \frac{1}{2}[(k - 1)\overline d^2 +\displaystyle \sum_{i = 1}^k {({n_i} - 1)(\overline d^2 - \overline d_i^2 )} ]$	$\overline {d}_i^2$ represents the average distance of samples in the ith cluster, $\overline { d}^2$ represents the average distance of all samples, and the larger the $ {\rm{CH}} $ , the better the clustering effect.

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Algorithm	Parameter setting
LSHADE	H=3, MF=0.5, p_init=0.11, N_init=200, N_min=150, D=2
CoBiDE	pb=0.4, ps=0.5, D=2
DE	F=0.5, Cr=0.5, D=2
SSA	Leader position update probability=0.5
PSO	C₁=1.5, C₂=1.5, D=2, Inertia factor=0.3
HHO	$ \alpha $ =0.01, $ \beta $ =1.5, J=2(1-rand)
GWO	a=2−2t/t_max
GSA	α=20, G0=100, R_norm=2, R_power=1

Number	Algorithm	Parameter setting
1	DPC	Reference [36]
2	FCM	The index of the membership matrix is 2, the maximum number of iterations is 200, and the minimum membership is 1.0e−5
3	GMM	The non-negative regularization number is 1.0e−5
4	CLA	Reference [37]
5	LGC	Reference [37]
6	K-means	Use Euclidean distance, the number of clusters is 4
7	GBK-Means	Reference [38]
8	C-LSHADE-Means	Use Euclidean distance, the number of clusters is 4, the maximum number of iterations is 200, CR=0.5,F=0.5, the initial population is 200, and the minimum population is 150

Data	Index	DPC	FCM	GMM	CLA	GLA	K-means	CBK-Means	C-LSHADE-Means
UCI Data	Ac/%	63.24	74.57	80.35	84.1	84.2	75.43	77.43	86.86
	DB	0.6584	0.7850	0.7701	0.635	0.635	0.7627	0.750	0.7526
	CH	237.3047	339.5852	283.9508	336.248	336.249	340.2413	286.260	340.2610
	Sil	0.2468	0.4160	0.4084	0.402	0.410	0.4192	0.383	0.4348
	Rank	8/8	3/8	5/8	6/8	4/8	2/8	7/8	1/8

Algorithm	Parameter setting
HSVM	H=2, C=100, $ \alpha = 1.0{\rm{e}} - 5 $
CSA-HSVM	Fl=2.5, AP=0.1, iter=100, NP=20, C=100, $\alpha = 1.0{\rm{e}} - 5$
KNN	K=4
LVQ	$ \eta = 0.01 $ , max_epoch=300, Num_Compet=20
SAE-HSVM	Number_AE=2,H=2, C=100, $ \alpha = 1.0{\rm{e}} - 5 $
SAE-LVQ	Number_AE=2, $ \eta = 0.01 $ , max_epoch=300, Num_Compet=20

Classifier	Mean (SD)
Classifier	Accuracy/%	RMSE	MAPE/%	Kappa	Time
HSVM	86.704(4.29e−01)	0.1208 (1.12e−02)	4.0262(4.61e−01)	0.7912 (6.4e−03)	9.183e−05 (1.862e−05)
LVQ	94.663(2.81e−01)	0.0468 (4.3e−03)	2.6607(1.95e−01)	0.9113 (4.7e−03)	3.412e−05 (3.247e−06)
CSA-SVM	94.10(7.43e−01)	0.062 (9.7e−03)	4.681(4.56e−01)	0.904 (1.20e−02)	3.16e−04 (7.34e−04)
KNN	88.015(6.13e−01)	0.055 (1.27e−02)	6.99(7.43e−01)	0.804 (1.74e−02)	7.674e−05 (5.152e−05)
SAe−HSVM	96.910(2.81e−01)	0.0356 (5.8e−03)	2.1223(5.49e−01)	0.9486 (4.6e−03)	4.435e−04 (6.882e−07)
SAe−LVQ	99.251(1.62e−01)	0.0037 (1.6e−03)	0.5384(1.46e−01)	0.9876 (2.7e−3)	1.024e−04 (1.292e−05)

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

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Situation	State1	State2	State3	State4	State5	State6	State7	State8
UCAV	1	1	2	2	3	3	4	4
Enemy	1	4	2	3	2	3	1	4
Global situation	Mutual threaten	Advantage	Mutual threaten	Advantage	Disadvantage	Mutual safe	Disadvantage	Mutual safe

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