在 BeiDou 导航卫星系统中的广播短片时间序列的评估和异常检测方法
Jiawei Cai1, Jianwen Li1, Shengda Xie1
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Liuxia Street, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种自动异常检测模型,用于北斗卫星导航系统 (BDS) 广播的短暂数据. IF-TEA-LSTM模型提高了探测精度,提高了卫星操作可靠性.
科学领域:
- 卫星导航系统 卫星导航系统
- 地测和地球物理学的地质学.
- 数据科学和机器学习
背景情况:
- 广播短暂数据对北斗导航卫星系统 (BDS) 至关重要,但容易受到来自电离层/热层影响,太阳辐射和时钟偏差的异.
- 传统的异常检测方法与不同类型的卫星 (GEO,IGSO,MEO) 的复杂性和独特特征作斗争.
研究的目的:
- 为BDS广播短暂数据开发一种自动化,强大的异常检测方法.
- 提高BDS中卫星运行的精度和可靠性.
主要方法:
- 提出了IF-TEA-LSTM模型,用于在广播短暂数据中检测异常.
- 将短暂数据转换为多变量时间序列,整合异常分数序列.
- 通过数据完整性评估和静态性测试,提高了检测稳定性.
主要成果:
- IF-TEA-LSTM模型将轨道参数的根平均平方误差 (RMSE) 降低了高达20.80%.
- 在短期预测中,中型地球轨道 (MEO) 卫星的时钟偏差预测精度提高了68.37%.
- 与基线模型相比,在GEO,IGSO和MEO卫星轨道上表现出卓越的性能.
结论:
- IF-TEA-LSTM模型显著提高了BDS广播短暂数据的异常检测精度.
- 该方法在长期数据处理方面被证明是有效的,提高了BDS卫星操作的整体可靠性.
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