Abstract
For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed "HASDM-ML," "CHAMP-ML," and "MSIS-UQ" machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density values. We develop several Monte Carlo methods, each capturing a different spatiotemporal density correlation, to study the effects of density uncertainty on orbit uncertainty propagation. However, Monte Carlo analysis is computationally expensive, so a faster method based on the Kalman filtering technique for orbit uncertainty propagation is also explored. It is difficult to translate the uncertainty in atmospheric density to the uncertainty in orbital states under a standard extended Kalman filter or unscented Kalman filter framework. This work uses the so-called "consider covariance sigma point (CCSP)" filter that can account for the density uncertainties during orbit propagation. As a testbed for validation purposes, a comparison between CCSP and Monte Carlo methods of orbit uncertainty propagation is carried out. Finally, using the HASDM-ML, CHAMP-ML, and MSIS-UQ density models, we propose an ensemble approach for orbit uncertainty quantification for four different space weather conditions.
Recommended Citation
S. N. Paul et al., "Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density," Advances in Space Research, vol. 71, no. 6, pp. 2535 - 2549, Elsevier, Mar 2023.
The definitive version is available at https://doi.org/10.1016/j.asr.2022.12.056
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
Atmospheric drag; Density uncertainty; Ensemble modeling; Machine learning; Orbit uncertainty quantification
International Standard Serial Number (ISSN)
1879-1948; 0273-1177
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Elsevier, All rights reserved.
Publication Date
15 Mar 2023
