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 prediction strategy that uses the normalized Euclidean distance to switch from the UKF (moment-based) to the PF (sample-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 during prediction, enabling complete independence from measurement updates. We demonstrate the capability of the approach by propagating the state uncertainty of the asteroid 2024 YR4 from its 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 prediction method effectively balances and integrates the complementary strengths of the moment- and sample-based approaches in a measurement-free environment.
Thermospheric mass density knowledge remains a primary driver of operational uncertainty for Low Earth Orbit operations, yet current sources of density observations suffer from limited latency, accuracy, and/or licensing issues. The growing body of Precise Orbit Determination data from emerging Low Earth Orbit constellations offers a promising source of thermospheric mass density measurements. Realising this potential requires the application of methods which can convert Precise Orbit Determination data into reliable density observations. This work provides an assessment of the Energy Dissipation Rate technique. Using a simulated constellation of up to eighteen Starlink-like satellites, we quantify the relationship between the quality of retrieved mass densities and four major influencing factors: Precise Orbit Determination noise level, drag acceleration magnitude, estimation fit-span, and the number of co-orbital spacecraft whose independent density estimates are averaged. Densities retrieved via the Energy Dissipation Rate method are also compared with densities that have been assimilated into a background density model. We identify conditions under which non-assimilated retrievals can outperform their assimilated counterparts, demonstrating the benefits of averaging data from multiple satellites along the same orbital track. Our results reveal three rules of thumb that describe how retrieval error scales as a function of fit-span, satellite count, and drag level. Doubling the fit-span roughly halves the RMS percentage error; adding co-orbital satellites reduces error as ∼N^-0.6 (unassimilated) or ∼N^-0.25 (assimilated); and drag-acceleration sensitivity follows a logistic response with the steepest gains between 2× 10^-8 and 2× 10^-7 m s^-2 . Together, these findings provide the first systematic quantification of how density-retrieval errors scale with Precise Orbit Determination noise, drag-acceleration level, fit-span, and satellite count. The resulting analytical approximation captures over 89
Previous studies have shown that δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} Scuti stars can be used for determining spacecraft position and time similar to X-ray pulsar navigation, but open questions remain regarding the light curve stability, and, therefore, the navigation accuracy that can be derived from δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} Scuti variable stars. Here, we develop a computational framework to identify δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} Scuti variable stars with light curves that are suitable for spacecraft navigation purposes. Our approach emphasizes quantifying timing uncertainty through developing metrics and evaluating such metrics in the context of spacecraft navigation. We analyze over 110 δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} Scuti variable stars from the Kepler space telescope and 10 additional stars from the K2 mission. For each star, we produce a simple model of its normalized flux as a function of time, along with several metrics used to assess its suitability for navigation. Model quality was further assessed through comparing predictions with observations from the Transiting Exoplanet Survey Satellite (TESS). Out of the 120 δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} Scuti variable stars we investigated in this study, 32 stars were identified as candidates predictable enough to enable spacecraft navigation.
The Earth–Moon system is constantly impacted by Solar System meteoroids causing threats to space and ground assets. A detailed characterization of meteoroid impact magnitudes, velocities, and distribution in time and space is fundamental for a proper space threats assessment and mitigation. The lunar surface is a natural detector of such impact phenomena. The LUMIO CubeSat mission aims to observe and characterize meteoroid impacts on the lunar farside, enhancing lunar situational awareness and complementing Earth-based observations of the lunar near-side. This study presents the mission design and analysis of LUMIO’s transfer trajectory, which exploits weak stability boundary dynamics to achieve a cost-effective insertion into the target halo orbit. Leveraging weak stability boundaries reduces the required spacecraft propellant and increases the number of launch opportunities. While focusing on the specific LUMIO mission, this work also highlights key features of the weak stability boundary transfer design which might prove useful for future cislunar missions, and especially for miniaturized platforms.
