Actions for Efficacy of Gaussian Process Regression for Angles-Only Initial Orbit Determination

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Efficacy of Gaussian Process Regression for Angles-Only Initial Orbit Determination

Author
Schwab, David
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2020.
Physical Description
1 electronic document
Additional Creators
Singla, Puneet
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Open Access.
Summary
Vital for space situational awareness, Initial Orbit Determination (IOD) may be used to initialize object tracking and associate observations with a tracked satellite. These classical IOD algorithms provide only a point solution and have been shown to be sensitive to noisy measurements and to certain target-observer geometry. First, the effects of measurement noise is investigated with respect to the classical Gauss-Gibbs IOD algorithm in various orbit regimes. While sensitive to the input noise in general, this classical method completely degenerates near a coplanar orbit, i.e. an observer-target geometry that induces linearly dependent line-of-sight measurements, which is a well studied failure mode of most minimum-measurement angles-only IOD algorithms. In an effort to bypass the sensitivity to noise and the case of coplanar degradation, this work utilizes an supervised learning algorithms known as Gaussian Process Regression (GPR). In this work, two multi-variate GPR frameworks are trained to perform angles-only orbit determination: 1) a simple model that assumes the predicted outputs are independent (ind-GPR) and 2) a complex model that accounts for output correlations (MV-GPR). The ability of both GPR methods to accurately predict the Cartesian state of the target is then benchmarked against the classical Gauss-Gibbs method for both perfect and noisy measurements, showing GPR prediction accuracy is robust to measurement noise and provides orders of magnitude improvement for prediction near the coplanar orbit case. Additionally, GPR not only provides a point-estimate for the IOD process but also characterizes the associated model uncertainty. The accuracy of this uncertainty characterization is investigated and compared between the two GPR methods. Numerical results show that while the added complexity of MV-GPR shows limited gains in estimation accuracy compared to ind-GPR, significant improvement is seen in the characterization of model uncertainty in the presence of perfect measurements.
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Dissertation Note
M.S. Pennsylvania State University 2020.
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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