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<p>Precise orbit determination (POD) for Low Earth Orbit (LEO) satellites constitutes a fundamental technological foundation for high-precision satellite services, with its accuracy directly governing the integrity of the satellite spatiotemporal datum and the overall performance of real-time applications. Under geomagnetic storm conditions, intense fluctuations in thermospheric density and ionospheric disturbances triggered by solar activity significantly degrade the quality of GNSS observation data and perturb dynamic models, leading to a severe deterioration in orbit determination accuracy. To address this challenge, we propose a geomagnetic-activity-driven adaptive LEO POD framework under geomagnetic storm conditions. POD experiments were conducted using real observation data from the GRACE-FO-C satellite during the intense geomagnetic storm of May 2024. The results demonstrate that after implementing segment-wise optimization of empirical accelerations and atmospheric drag parameters in the dynamic model, the three-dimensional root-mean-square (3D RMS) orbit determination errors decreased from 0.373&#xa0;m to 0.119&#xa0;m and 0.116&#xa0;m, corresponding to improvements of 68.1 and 68.9%, respectively. Furthermore, an adaptive stochastic model based on the SYM-H index and observation residuals was introduced, and the 3D RMS orbit determination errors were further reduced to 0.112 and 0.077&#xa0;m, achieving improvements of 70.0 and 79.5%, respectively. Through the integration of segment-wise dynamic model optimization and adaptive stochastic model adjustment, the orbit determination error during geomagnetic storms was ultimately restored to centimeter level accuracy, approximating the nominal precision under quiescent geomagnetic conditions. Additional validation on GRACE‑A and SWARM‑A data demonstrates the methods’ generalizability. This study provides an effective solution for robustly improving the precision of LEO POD under extreme geomagnetic storm conditions.</p>

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Improved methods for precise orbit determination of LEO satellites under geomagnetic storm conditions

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Tianyu Fan,
  • Ran Li,
  • Ying Xu,
  • Haitao Wu,
  • Guang Yang,
  • Wen Li

摘要

Precise orbit determination (POD) for Low Earth Orbit (LEO) satellites constitutes a fundamental technological foundation for high-precision satellite services, with its accuracy directly governing the integrity of the satellite spatiotemporal datum and the overall performance of real-time applications. Under geomagnetic storm conditions, intense fluctuations in thermospheric density and ionospheric disturbances triggered by solar activity significantly degrade the quality of GNSS observation data and perturb dynamic models, leading to a severe deterioration in orbit determination accuracy. To address this challenge, we propose a geomagnetic-activity-driven adaptive LEO POD framework under geomagnetic storm conditions. POD experiments were conducted using real observation data from the GRACE-FO-C satellite during the intense geomagnetic storm of May 2024. The results demonstrate that after implementing segment-wise optimization of empirical accelerations and atmospheric drag parameters in the dynamic model, the three-dimensional root-mean-square (3D RMS) orbit determination errors decreased from 0.373 m to 0.119 m and 0.116 m, corresponding to improvements of 68.1 and 68.9%, respectively. Furthermore, an adaptive stochastic model based on the SYM-H index and observation residuals was introduced, and the 3D RMS orbit determination errors were further reduced to 0.112 and 0.077 m, achieving improvements of 70.0 and 79.5%, respectively. Through the integration of segment-wise dynamic model optimization and adaptive stochastic model adjustment, the orbit determination error during geomagnetic storms was ultimately restored to centimeter level accuracy, approximating the nominal precision under quiescent geomagnetic conditions. Additional validation on GRACE‑A and SWARM‑A data demonstrates the methods’ generalizability. This study provides an effective solution for robustly improving the precision of LEO POD under extreme geomagnetic storm conditions.