一个统一的GNN-CV框架用于智能空中情境意识.
Leyan Li1, Rennong Yang1, Anxin Guo1
1Air Traffic Control and Navigation School, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了一个统一的图形神经网络-计算机视觉框架,以增强空中情境意识 (SA). 该方法在识别空中构造方面达到90%以上的准确性,改善了复杂动态环境的决策.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 航空航天工程 航空航天工程
背景情况:
- 空中情境意识 (SA) 是复杂的,涉及动态实体和时空关系.
- 目前对SA的深度学习方法往往缺乏一个整体的,以视觉为中心的方法,这对于人类决策至关重要.
- 现有的模型难以处理各种数据模式,并将其集成为全面的SA.
研究的目的:
- 开发一个统一的图形神经网络-计算机视觉 (GNN-CV) 框架,用于操作级空中SA.
- 弥合深度学习能力与有效决策所需的人为中心视角之间的差距.
- 用成熟的计算机视觉架构处理类似雷达地图的表示,用于各种SA任务.
主要方法:
- 提出了一个统一的GNN-CV框架,集成稀疏实体属性转换图神经网络 (SET-GNN).
- 开发了用于大规模雷达地图重建和综合特征提取的方法.
- 雇佣了专门的两阶段预培训和可适应的下游任务网络,用于空中SA.
主要成果:
- 实现了超过90.1%的端到端识别准确度,用于空中小群分区和配置识别.
- 在具有不同飞行间隔和组成类型的战术场景中,配置识别精度超过了85.0%.
- 即使有重大位置/定位干扰,也保持了超过80.4%的准确性,在毫秒响应周期内运行.
结论:
- 利用成熟的计算机视觉技术显著提高了智能SA的有效性,弹性和连贯性.
- 拟议的GNN-CV框架为复杂的操作级空中SA任务提供了强大的解决方案.
- 该框架在具有挑战性的条件下识别空中阵列方面表现出卓越的表现.
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