Non-Gaussian Recursive Bayesian Filtering for Outer Planetary Orbilander Navigation

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., Rosengren, A.J., Bewley, T.R., Ely, T.A.: Non-Gaussian recursive Bayesian filtering for outer planetary orbilander navigation. In: AAS/AIAA Space Flight Mechanics Meeting, p. 194 (2025)
Download Paper