Astrodynamics is the study of orbital motion and the physical principles that govern the behavior of spacecraft and celestial bodies. In the NEAR group, our astrodynamics research bridges the gap between classical orbital mechanics and modern uncertainty-aware estimation, enabling more accurate and robust orbit determination in the face of sparse or noisy data. We develop advanced filtering techniques that integrate machine learning concepts such as kernel methods and Gaussian mixtures into ensemble-based frameworks, providing improved performance for tracking and prediction in complex dynamical environments.
Particular areas of interest include orbit determination with sparse ground-based observations, optimal sampling strategies for high-fidelity point mass filtering, and the use of quaternion-based representations for rotational dynamics. We also examine foundational issues in rotational kinematics and frame transformations, as well as spacecraft rendezvous and guidance problems.
Key Papers
- Kernel‑Based Ensemble Gaussian Mixture Filtering for Orbit Determination with Sparse Data – Applies kernel-enhanced EnGMF to orbit determination using sparse measurements with improved accuracy.
- Rotations, Transformations, Left Quaternions, Right Quaternions? – Discusses quaternion conventions and their implications for attitude estimation and spacecraft control.
- Optimal Sampling for Point Mass Filtering with Applications to Cislunar Orbit Determination – Proposes sampling strategies for point-mass filters to enhance orbit determination in the cislunar environment.