State Representations for Measurement Fusion and Uncertainty Propagation in Cislunar Regime

The Artemis program, and specifically the Gateway mission, will leverage near-rectilinear halo orbits (NRHOs) as they allow for convenient transfers to the Earth, lunar orbits, and the lunar surface. Currently, the primary source of navigation for Gateway is the Deep Space Network (DSN), which incurs lengthy outages between passes, leading to non-Gaussian uncertainty distributions depending on the chosen coordinate system. To account for the substantial nonlinearities present when representing states in cislunar space, for Gateway and other missions, we propose a series of techniques to mitigate the nonlinear effects by jointly considering the measurement fusion and uncertainty propagation components. We hypothesize that coordinate transformations will be advantageous along with nonlinear filtering techniques including particle representations, Gaussian mixture models (GMMs), state transition tensors (STTs), and other novel techniques. The proposed research could reduce dependency on the DSN while also increasing the robustness of orbit determination, collision avoidance, the general optimal control problem, and trajectory optimization for cislunar applications. By handling measurement fusion and uncertainty propagation in a coupled approach, we anticipate an improved estimate of the spacecraft's true state and a better representation of the associated uncertainty within the chaotic cislunar environment.

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