Non-Gaussian Recursive Bayesian Filtering for Outer Planetary Orbilander Navigation
Published in AAS/AIAA Space Flight Mechanics, 2025
Abstract: Gaussian estimation filters have successfully aided spacecraft navigation for decades. However, future deep-space missions plan to operate orbilanders in unstable quasiperiodic orbits and perform low-altitude flybys of outer planetary moons. These complex trajectories may necessitate non-Gaussian filtering for accurate estimation over realistic measurement cadences. To mitigate the inherent risk associated with testing novel navigation software, non-Gaussian filters must be accurate, efficient, and robust. A novel Eulerian approach, Grid-based, Bayesian Estimation Exploiting Sparsity, along with the contemporary landscape of filters, are evaluated on these criteria through a Bayesian investigation, wherein the state uncertainty of a Saturn-Enceladus Distant Prograde Orbit is propagated.
Recommended citation: Hanson, B. L., Ely, T. A., Bewley, T. R., and Rosengren, A. J., "Non-Gaussian Recursive Bayesian Filtering for Outer Planetary Orbilander Navigation," AAS/AIAA Space Flight Mechanics Meeting, 2025, pp. 25–194
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