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

TL;DR

This paper presents a real-time thermospheric density estimation method using TLE data and a reduced-order model, improving orbit prediction accuracy and enabling density forecasting.

Contribution

It introduces a novel reduced-order dynamic model combined with an unscented Kalman filter for real-time density estimation from TLE data.

Findings

Accurate density estimates validated against CHAMP and GRACE data.

Effective density forecasting demonstrated.

Model improves low Earth orbit prediction accuracy.

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.