Real-Time Thermospheric Density Estimation Via Two-Line-Element Data Assimilation

Abstract

Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. To improve orbit prediction, real-time density estimation is required. In this work, we develop a reduced-order dynamic model for the thermospheric density by computing the main spatial modes of the atmosphere and deriving a linear model for the dynamics. The model is then used to estimate the density using two-line element (TLE) data by simultaneously estimating the reduced-order modes and the orbits and ballistic coefficients of several objects using an unscented Kalman filter. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer-derived densities. Finally, the use of the model for density forecasting is shown.

arXiv comment24 pages, 13 figures, preprint

References in corpus (4)

• Orbit Determination and Differential-drag Control of Planet Labs Cubesat Constellations
• A quasi-physical dynamic reduced order model for thermospheric mass density via Hermitian Space Dynamic Mode Decomposition
• A new transformative framework for data assimilation and calibration of physical ionosphere-thermosphere models
• Data-driven framework for real-time thermospheric density estimation

Cited by in corpus (3)

• Machine Learning in Orbit Estimation: a Survey
• Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models
• Machine-Learned HASDM Model with Uncertainty Quantification