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Mechanical and Aerospace Engineering Faculty Research & Creative Works

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2023

Orbit uncertainty quantification

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Full-Text Articles in Engineering

Stochastic Modeling Of Physical Drag Coefficient – Its Impact On Orbit Prediction And Space Traffic Management, Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata, Piyush M. Mehta Aug 2023

Stochastic Modeling Of Physical Drag Coefficient – Its Impact On Orbit Prediction And Space Traffic Management, Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating better space domain awareness (SDA). Correct modeling of uncertainties in force models and orbital states, among other things, is an essential part of SDA. For objects in the low-Earth orbit (LEO) region, the uncertainty in the orbital dynamics mainly emanate from limited knowledge of the atmospheric drag-related parameters and variables. In this paper, which extends the work by Paul et al. (2021), we develop a …


Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density, Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta Mar 2023

Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density, Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

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 …