Thermospheric density predictions during quiet time and geomagnetic storm using a deep evidential model-based framework
Thermospheric density predictions during quiet time and geomagnetic storm using a deep evidential model-based framework
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DOI：
10.1016/j.actaastro.2023.06.023
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发表时间：
2023-10
影响因子：
3.5
通讯作者：
Yiran Wang;X. Bai
中科院分区：
文献类型：
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作者：
Yiran Wang;X. Bai
了解热层密度对于计算近地轨道卫星的阻力至关重要。现有模型很难准确预测密度。在本文中，我们提出使用基于深度证据模型的框架进行热层密度预测，该框架结合了经验模型、CHAMP 卫星的加速度计推断的密度以及地磁和太阳指数。该框架在安静和暴风雨条件下进行了研究。我们的结果表明，所提出的模型可以在安静和暴风雨时期以高精度和可靠的不确定性预测热层密度。证据模型的预测结果优于我们之前研究中的高斯过程（GP）模型。此外，所提出的模型还可以提供有洞察力的任意和认知不确定性。
Knowledge of the thermospheric density is essential for calculating the drag in low Earth orbit satellites. Existing models struggle to predict density accurately. In this paper, we propose thermospheric density prediction using a deep evidential model-based framework that incorporates empirical models, accelerometer-inferred density from the CHAMP satellite, and geomagnetic and solar indices. The framework is investigated on both quiet and storm conditions. Our results demonstrate that the proposed model can predict the thermospheric density with high accuracy and reliable uncertainty in both quiet and storm times. The predicted results from the evidential model are advantageous over the Gaussian Processes (GPs) model in our previous studies. Furthermore, the proposed model can also provide insightful aleatoric and epistemic uncertainties.