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

Authors

• Peng He, Graduate School, National Defense University, Beijing 100091, China
• Yubo Tang, Joint Operations College, National Defense University, Beijing 100091, China
• Jingde Liu, Joint Operations College, National Defense University, Beijing 100091, China
• Yujia Tian, Graduate School, National Defense University, Beijing 100091, China

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 semisupervised 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.

Recommended Citation

He, Peng; Tang, Yubo; Liu, Jingde; and Tian, Yujia (2026) "Intelligent Evaluation Method for Regional Air Superiority Based on Graph Convolutional Neural Network," Journal of System Simulation: Vol. 38: Iss. 8, Article 12.
DOI: 10.16182/j.issn1004731x.joss.25-0936
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss8/12

First Page

2294

Last Page

2311

CLC

E816; TP391

Recommended Citation

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

Corresponding Author

Tang Yubo

DOI

10.16182/j.issn1004731x.joss.25-0936