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Atmospheric density estimation for improved orbit determination and conjunction assessment

David Gondelach1,Srinivas Setty1,Christoph Bamann1,Paul Cefola2
Vyoma GmbH1State University of New York at Buffalo2

Document details

Publishing year2021 PublisherESA Space Debris Office Publishing typeConference Name of conference8th European Conference on Space Debris
Pagesn/a Volume
T. Flohrer, S. Lemmens, F. Schmitz


Accurate knowledge of the orbits of all Earth-orbiting objects is required to enable space traffic management for safe space operations. However, due to the limited accuracy of dynamical models and orbital tracking data, orbit estimates are often not sufficiently accurate to avoid the problem of probability dilution in satellite conjunction analysis.
Atmospheric drag is the largest source of error in orbit determination and prediction for low Earth orbits. Most of the uncertainty stems from inaccurate knowledge of atmospheric density [1]. The uncertainties in the density are due to errors in both the model and the input, such as space weather indices. For orbit determination and short-term orbit prediction (<2 days) errors due to model inaccuracy tend to dominate, whereas errors due to inputs become dominant for longer orbit prediction when predicted space weather data are used. Therefore, the accuracy of orbit determination can be improved by reducing the error of density estimates. This can be achieved by calibrating density models or by estimating thermospheric densities directly using orbital tracking data. Such approaches can significantly reduce the bias and uncertainty of atmospheric densities.

Thermospheric density corrections have been computed in the past using two-line element (TLE) data by comparing observed and estimated ballistic coefficients [2] or comparing observed and estimated changes in the orbital mean motion [3]. Recently, a new technique for density estimation and prediction was developed by deriving and applying reduced-order models for the thermospheric density [4,5]. The developed reduced-order models have been used with Kalman filters to accurately estimate the global thermospheric density through data assimilation of density and orbital data [6,7,8]. Density estimation was demonstrated by assimilating accelerometer-derived density measurements [6], TLE data [7] and radar and GPS tracking data [8]. In particular, it was shown that density estimates based on TLE data are more accurate than the JB2008 and NRLMSISE-00 models when compared with CHAMP and GRACE accelerometer-derived and SWARM GPS-derived densities [7,8]. Moreover, the technique allows for uncertainty quantification of the density estimates as well as prediction of densities and their uncertainty [9].

In this paper, we assess the improvement of orbit determination accuracy due to the use of estimated densities obtained using reduced-order models. First, we estimate thermospheric densities using TLE data and quantify the uncertainty in the estimated density by comparison against accurate density data. Then the densities are used for orbit determination and the resulting orbit solution is compared against accurate ephemeris and against solutions obtained by applying empirical density models. In addition, we compare the statistics of series of estimated ballistic coefficients obtained using estimated and modelled densities to analyze the effect of density calibration on estimated ballistic coefficients. Finally, the accuracy of orbit predictions is assessed and their use for conjunction assessment considering density uncertainties is demonstrated.


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