Angles-Only Initial Orbit Determination via Multivariate Gaussian Process Regression
Abstract
Vital for Space Situational Awareness, Initial Orbit Determination (IOD) may be used to initialize object tracking and associate observations with a tracked satellite. Classical IOD algorithms provide only a point solution and are sensitive to noisy measurements and to certain target-observer geometry. This work examines the ability of a Multivariate GPR (MV-GPR) to accurately perform IOD and quantify the associated uncertainty. Given perfect test inputs, MV-GPR performs comparably to a simpler multitask learning GPR algorithm and the classical Gauss–Gibbs IOD in terms of prediction accuracy. It significantly outperforms the multitask learning GPR algorithm in uncertainty quantification due to the direct handling of output dimension correlations. A moment-matching algorithm provides an analytic solution to the input noise problem under certain assumptions. The algorithm is adapted to the MV-GPR formulation and shown to be an effective tool to accurately quantify the added input uncertainty. This work shows that the MV-GPR can provide a viable solution with quantified uncertainty which is robust to observation noise and traditionally challenging orbit-observer geometries.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 15, 2022
- Source ID
- 10.3390/electronics11040588
Entities
People
- David J. Schwab
- Puneet Singla
- Sean O’rourke
Organizations
- Air Force Office of Scientific Research
- Department of Defence
- United States Air Force