Hybrid, Ephemeris-Quality, Measurement-Free Estimation of the Potential 2024 YR4 Lunar Impact

For perturbed two-body motion—such as small bodies in heliocentric orbits—the Cartesian state uncertainty alternates between near-Gaussian and strongly non-Gaussian regimes over the orbit. This behavior motivates hybrid estimators that adapt to the changing character of the uncertainty. Implementation of a hybrid filter hinges on the mechanisms that control the transitions between the two frameworks. In this paper, we introduce an unscented Kalman filter/particle filter (UKF/PF) hybrid estimation strategy that uses the normalized Euclidean distance to switch from the UKF (moment-based) to the PF (ensemble-based), and the Henze-Zirkler statistic to switch back, with dynamics sourced from ASSIST, an ephemeris-quality integrator. This autonomous switching technique requires only the abstractions propagated by the respective filters, enabling complete independence from measurement updates. We demonstrate the capability of the filter by propagating the state uncertainty of the asteroid 2024 YR4 from its last reported orbit solution in November 2025 to its close approach of the Moon in December 2032. The hybrid UKF/PF accurately and efficiently predicts the lunar impact probability, velocity, angle, and potential cratering of 2024 YR4, providing all the information necessary to assess the potential fallout of the ejecta created from such an impact event. The proposed hybrid approach effectively balances and integrates the complementary strengths of both filtering frameworks in a measurement-free environment.