Extended Kalman Filtering in Burdet Coordinates for Orbit Determination

Open Access
- Author:
- Ciliberto, David
- Graduate Program:
- Aerospace Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- April 26, 2019
- Committee Members:
- Puneet Singla, Thesis Advisor/Co-Advisor
Amy Ruth Pritchett, Committee Member - Keywords:
- Kalman Filtering
Estimation
Orbit Determination
Space Situational Awareness
Regularization - Abstract:
- The objective of this thesis is to develop a formulation of the extended Kalman filter (EKF) in regularized coordinates for use in orbit determination. Regularizing transformations have useful properties for solving astrodynamics problems and this thesis seeks to leverage them in an estimation context. The main motivation is reduced nonlinearity in the equations of motion, uncertainty propagation, and the measurement model which would lessen the negative impact of linearization during EKF estimation. Drawbacks of this approach, including added dimensionality and nonlinear independent variable transformation, are discussed and addressed. Regularized coordinate systems are defined and discussed followed by a brief history of orbit estimation and a derivation of the extended Kalman filter. A Kalman filtering algorithm is developed in the regularized space by making use of key properties of the transformation followed by a discussion on the unique challenges associated with orbit estimation in the transformed space. Simulated orbit determination is conducted in the regularized space and using a traditional Cartesian extended Kalman filter for comparison. Estimation in the regularized space is shown to offer significantly improved performance over the Cartesian EKF when long-term propagation is required.