Intelligent Evaluation Method for Regional Air Superiority Based on Graph Convolutional Neural Network

doi:10.16182/j.issn1004731x.joss.25-0936

Abstract

Abstract:

To address the dual challenges of the lack of quantification standards and sparse air combat samples in the superiority evaluation of modern air combat, an intelligent evaluation method for regional air superiority based on a graph convolutional neural network was proposed. The spatial grid method was employed to encode the air situation of the entire battlefield airspace into a new data representation, namely the air superiority matrix. A novel air superiority quantification model was applied to quantify the air superiority label values of spatial grids, and four key operational capabilities closely related to air superiority contention were selected as the attribute features of spatial grid nodes. Based on the semi-supervised learning capability of a graph neural network, a graph convolutional neural network model fused with the label propagation algorithm (LPA), named GCN-LPA, was designed. LPA was utilized to generate high-quality pseudo-labels, which were concatenated with the original features and then input into the GCN network to form a feature enhancement mechanism. The K-fold cross-validation method was adopted to enhance the generalization ability and training effect of the model. The GCN-LPA model can generate a full-domain battlefield air superiority distribution heatmap in real time, providing precise situational support for command decision-making. The experimental results indicate that this method is significantly superior to the baseline model on the air superiority dataset, and the effect of the feature enhancement strategy based on LPA pseudo-labels is significantly better than that of the strategy of introducing an LPA constraint term into the GCN loss function.

Key words: air superiority evaluation, situation awareness, command and control, graph convolutional neural network, label propagation

CLC Number:

E816

He Peng, Tang Yubo, Liu Jingde, Tian Yujia. Intelligent Evaluation Method for Regional Air Superiority Based on Graph Convolutional Neural Network[J]. Journal of System Simulation, 2026, 38(8): 2294-2311.

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实验分组	编号	模型名称	LPA特征	LPA损失
对照组	A0	GCN	×	×
对照组	A1	GCN-LPA-LOSS	×	√
实验组	A2	GCN-LPA	√	×
实验组	A3	GCN-LPA-FULL	√	√

参数名称	参数值	参数名称	参数值
隐藏层维度	256	Dropout率	0.100 4
输出层维度	16	学习率	0.004 2
神经网络层数	3	权重衰减系数	1.655 6×10^-5
批量大小	64	训练周期	100

编号	E_MS	E_MA	R²
A0	0.154 7/0.142 3	0.169 4/0.155 1	0.601 6/0.642 5
A1	0.132 4/0.141 6	0.143 1/0.144 6	0.624 5/0.644 5
A2	0.044 3/0.041 9	0.061 2/0.061 0	0.881 5/0.894 8
A3	0.042 6/0.042 0	0.051 5/0.064 9	0.885 7/0.894 5

类别	模型名称	E_MS	E_MA	R²
ML	LPA	0.795 0/0.796 3	0.888 3/0.884 9	-2.249 2/-2.402 1
	Ridge回归	0.011 5/0.017 6	0.021 9/0.029 0	0.094 8/0.101 6
	KNN	0.011 6/0.017 2	0.022 0/0.034 1	0.102 7/0.122 6
	随机森林回归	0.011 1/0.017 2	0.021 2/0.030 5	0.109 9/0.131 6
	贝叶斯回归	0.011 5/0.017 6	0.021 8/0.029 1	0.094 7/0.101 6
MLP	MLP	0.011 5/0.017 7	0.020 8/0.027 1	0.094 3/0.098 8
MLP	Graph-MLP	0.011 5/0.017 7	0.020 8/0.027 1	0.094 3/0.098 8
GNN	GAT	0.183 2/0.161 5	0.222 6/0.182 2	0.489 2/0.549 3
	SGC	0.132 9/0.140 6	0.144 9/0.141 0	0.623 1/0.646 7
	GraphSAGE	0.146 4/0.142 7	0.148 8/0.143 9	0.605 9/0.641 7
	GCN-LPA	0.036 8/0.042 1	0.048 7/0.050 8	0.895 7/0.894 1

参数类型	参数名称	优化区间
离散参数	隐藏层维度	{32,64,128,256}
	输出层维度	{16,32,64}
	批量大小	{4,8,16,32,64}
	神经网络层数	{2,3,4,5,6}
连续参数	学习率	[1×10^-5,1×10^-2]
	Dropout率	[0.1,0.7]
	权重衰减系数	[1×10^-6,1×10^-3]
	训练周期	[50,200]