[1]ZHOU Xianchen,MA Yang,CHENG Guangquan,et al.Robust graph neural network method for target entity type recognition in a battlefield environment[J].CAAI Transactions on Intelligent Systems,2023,18(6):1156-1164.[doi:10.11992/tis.202111025]
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Robust graph neural network method for target entity type recognition in a battlefield environment

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1156-1164 Column: 学术论文—机器学习 Public date: 2023-11-05
Title:
Robust graph neural network method for target entity type recognition in a battlefield environment
Author(s):
ZHOU Xianchen1;  MA Yang2;  CHENG Guangquan2;  WANG Hongxia1
1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410072, China;
2. College of Systems Engineering, National University of Defense Technology, Changsha 410072, China
Keywords:
battlefield data; entity recognition; identification range; dynamic time warping; data mining; graph neural network; robustness; graph convolutional network
CLC:
TP391
DOI:
10.11992/tis.202111025
Abstract:
With the rise of new combat styles, such as information and algorithmic warfare, target entity recognition in battlefield data analysis plays an important role in decision making. Battlefield situation data are typical battlefield data containing many dynamic entities with close interactions. However, such data often contain strong noise due to hostile interference or concealment; hence, they require higher robustness than general time-series data. This paper proposes a new method based on graph neural networks to represent and process the unstructured data and mine the category information of hostile combat entities. First, the dynamic time warping algorithm was used to establish a new graph structure between combat entities based on their trajectory. Then, a robust graph neural network method was proposed and applied for the type identification of combat entities beyond the radar identification range according to the node attribute information of combat entities. Test results on the simulation data set obtained from the military simulation platform reveal that the proposed method maximizes the temporal characteristics of the entity data and associated attribute information of each node. Compared with the graph neural network and multilayer perceptron methods that rely on single-time relation, the proposed method has advantages in identification accuracy and robustness, expanding the radius of operational entity identification to a certain extent.
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