智能系统学报2023，Vol.18Issue(6)：1156-1164,9.DOI:10.11992/tis.202111025
战场目标实体类型识别的鲁棒图神经网络方法
Robust graph neural network method for target entity type recognition in a battlefield environment
摘要
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 in-formation of hostile combat entities.First,the dynamic time warping algorithm was used to establish a new graph struc-ture 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 attrib-ute 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 in-formation 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 op-erational entity identification to a certain extent.关键词
战场态势数据/实体识别/识别半径/动态时间规整/数据挖掘/图神经网络/鲁棒性/图卷积神经网络Key words
battlefield data/entity recognition/identification range/dynamic time warping/data mining/graph neural network/robustness/graph convolutional network分类
计算机与自动化引用本文复制引用
周贤琛,马扬,程光权,王红霞..战场目标实体类型识别的鲁棒图神经网络方法[J].智能系统学报,2023,18(6):1156-1164,9.基金项目
国家自然科学基金项目(61977065,62073333). （61977065,62073333）
