[1]周贤琛,马扬,程光权,等.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.[doi:10.11992/tis.202111025]
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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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]
战场目标实体类型识别的鲁棒图神经网络方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第6期
页码:
1156-1164
栏目:
学术论文—机器学习
出版日期:
2023-11-05
- Title:
- Robust graph neural network method for target entity type recognition in a battlefield environment
- Keywords:
- battlefield data; entity recognition; identification range; dynamic time warping; data mining; graph neural network; robustness; graph convolutional network
- 分类号:
- TP391
- 摘要:
- 随着信息战和算法战等新型作战样式的兴起，战场数据分析中的目标实体识别任务对决策起着重要作用。战场态势数据作为最典型的战场数据之一，包含了许多紧密交互的动态实体数据。但此类数据因敌方干扰或伪装常常含有较强的噪声，与一般时序关系数据相比，对目标实体方法的鲁棒性要求更高。本文基于图神经网络提出了一种表示和处理这类态势数据、识别敌方作战实体的新方法。首先，使用动态时间规整算法基于作战实体轨迹建立了作战实体之间的新型图结构数据模型，进而根据作战实体的节点属性信息提出了一种鲁棒的图神经网络方法，并将其应用于雷达识别范围之外的作战实体类型辨识。在兵棋推演平台获得的仿真数据集上的测试结果表明，本文方法由于充分利用了实体数据的时序特征以及关联的属性信息，与依赖单个时刻关系构建出的图神经网络方法以及多层感知机等方法相比，在识别精度、鲁棒性等方面优势明显，一定程度上扩大了作战实体识别的半径。
- 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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备注/Memo
收稿日期:2021-11-15。
基金项目:国家自然科学基金项目（61977065，62073333）.
作者简介:周贤琛,博士研究生,主要研究方向为图机器学习。参与科研项目3项,发表学术论文5篇;马扬,博士研究生,主要研究方向为网络分析。参与科研项目3项;王红霞,教授,主要研究方向为神经网络、计算成像、傅里叶相位恢复。主持科研项目9项,发表学术论文50余篇
通讯作者:王红霞.E-mail:wanghongxia@nudt.edu.cn
更新日期/Last Update:
1900-01-01