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


Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real-time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced-order model for the thermospheric density that enables real-time density estimation using two-line element (TLE) data. For this, the global thermospheric density is represented by the main spatial modes of the atmosphere and a time-varying low-dimensional state and a linear model is derived for the dynamics. Three different models are developed based on density data from the TIE-GCM, NRLMSISE-00, and JB2008 thermosphere models and are valid from 100 to maximum 800 km altitude. Using the models and TLE data, the global density is estimated by simultaneously estimating the density and the orbits and ballistic coefficients of several objects using a Kalman filter. The sequential estimation provides both estimates of the density and corresponding uncertainty. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer-derived densities. The estimated densities are shown to be significantly more accurate and less biased than NRLMSISE-00 and JB2008 modeled densities. The uncertainty in the density estimates is quantified and shown to be dependent on the geographical location, solar activity, and objects used for estimation. In addition, the data assimilation capability of the model is highlighted by assimilating CHAMP accelerometer-derived density data together with TLE data to obtain more accurate global density estimates. Finally, the dynamic thermosphere model is used to forecast the density.

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Space Weather
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