Scholarlink

<p>Accurate prediction of Low Earth Orbit (LEO) Satellite Clock Bias (SCB) is crucial for real-time precise applications of LEO-enhanced GNSS (LeGNSS). However, the inherent instability of oscillators aboard LEO satellites presents challenges for traditional model-driven SCB prediction methods, which struggle to capture the complex variability of LEO SCB sufficiently and thus cannot achieve high-accuracy solutions. To address this challenge, we propose a neural network-compensated SCB prediction method (NNC-SP), which builds on a periodic polynomial (PP) model for baseline prediction and applies an enhanced Informer model to compensate for unmodeled patterns. Two key innovations are introduced: (1) a Dimension-Segment-Wise (DSW) pre-embedding module that segments input data to better capture local temporal structures; and (2) a smoothed Mean Squared Error loss function that incorporates relative relationships within SCB sequences, beyond pointwise errors. The method is validated using one year of SCB data from the GRACE-FO C and D satellites. When trained with a 1-hour prediction arc, NNC-SP improves prediction accuracy by approximately 90% across all lengths. For GRAC, the RMSEs for 5-, 10-, 30-, and 60-minute predictions are 0.07, 0.12, 0.38, and 0.90 ns, respectively; for GRAD, the corresponding values are 0.14, 0.22, 0.67, and 1.52 ns. Further improvements are achieved by training for shorter arcs: for example, the RMSEs are 0.05 and 0.11 ns for GRAC and 0.08 and 0.17 ns for GRAD at 5- and 10-minute predictions, respectively. NNC-SP also demonstrates robust performance, outperforming PP in 95% of cases. These results confirm its promise for high-accuracy real-time SCB prediction in future LeGNSS applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neural network-compensated satellite clock bias prediction for Low Earth Orbit (LEO) satellites

  • 期刊论文

  • 发表日期：

    2026年1月21日

查看全文
  • Tianhao Wu,
  • Bofeng Li,
  • Haibo Ge

摘要

Accurate prediction of Low Earth Orbit (LEO) Satellite Clock Bias (SCB) is crucial for real-time precise applications of LEO-enhanced GNSS (LeGNSS). However, the inherent instability of oscillators aboard LEO satellites presents challenges for traditional model-driven SCB prediction methods, which struggle to capture the complex variability of LEO SCB sufficiently and thus cannot achieve high-accuracy solutions. To address this challenge, we propose a neural network-compensated SCB prediction method (NNC-SP), which builds on a periodic polynomial (PP) model for baseline prediction and applies an enhanced Informer model to compensate for unmodeled patterns. Two key innovations are introduced: (1) a Dimension-Segment-Wise (DSW) pre-embedding module that segments input data to better capture local temporal structures; and (2) a smoothed Mean Squared Error loss function that incorporates relative relationships within SCB sequences, beyond pointwise errors. The method is validated using one year of SCB data from the GRACE-FO C and D satellites. When trained with a 1-hour prediction arc, NNC-SP improves prediction accuracy by approximately 90% across all lengths. For GRAC, the RMSEs for 5-, 10-, 30-, and 60-minute predictions are 0.07, 0.12, 0.38, and 0.90 ns, respectively; for GRAD, the corresponding values are 0.14, 0.22, 0.67, and 1.52 ns. Further improvements are achieved by training for shorter arcs: for example, the RMSEs are 0.05 and 0.11 ns for GRAC and 0.08 and 0.17 ns for GRAD at 5- and 10-minute predictions, respectively. NNC-SP also demonstrates robust performance, outperforming PP in 95% of cases. These results confirm its promise for high-accuracy real-time SCB prediction in future LeGNSS applications.