搜索增强补丁生成用于地球同步卫星状态预测
Shu-He Tian1, Yu-Qiang Fang2, Ya-Sheng Zhang1
1Space Engineering University, Beijing, 101416, China.
Scientific reports
|February 2, 2026
概括
本研究介绍了RAPG,这是一个用于地球同步卫星状态预测和意图分类的新型框架. 通过有效地建模复杂的轨道动态和增强远程依赖学习,RAPG提高了空间局势意识.
科学领域:
- 空间 情境意识 空间 情境意识
- 卫星动力学 卫星动力学
- 航空航天领域的人工智能
背景情况:
- 准确的地球同步卫星状态预测对于空间局势意识至关重要.
- 目前的模型与非静止的,弱周期的高轨道数据作斗争,阻碍了远程依赖性学习和适应不断变化的轨道动态.
研究的目的:
- 提出RAPG (检索增强补丁生成),用于地球同步卫星状态分析的框架,包括预测和意图分类.
- 解决模拟复杂轨道动态的局限性,改善时间表示学习.
主要方法:
- RAPG使用自适应频域补丁机制,根据光谱特征对卫星状态时间序列进行细分.
- 检索增强生成模块访问历史数据以重建非重复的运动模式.
- 该框架将自适应补丁与基于检索的增强联系在一起,以增强时间学习.
主要成果:
- 在地球同步卫星状态预测和意图分类任务中,RAPG始终表现优于强大的基线.
- 该框架实现了最低的预测错误.
- 在意图分类方面,RAPG获得了0.9461的F1得分和0.9443的准确性.
结论:
- RAPG提供了一个强大的,可扩展的解决方案,用于对地球同步卫星实时分析.
- 该框架显著提高了基于遥感的空间领域意识和动态轨道智能.
- 在不同的轨道条件中,RAPG表现出了更好的概括性.
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