Recent lunar missions and future exploration initiatives have renewed interest in sustained operations around the Moon. Past missions such as Apollo and GRAIL have significantly advanced our understanding of the Moon’s mass distribution and orientation, enabling more accurate dynamical modeling and improved navigation in the lunar environment. The availability of high-resolution gravity field models, combined with the absence of an atmosphere, allows spacecraft to operate in low-altitude lunar orbits. However, the lack of navigation aids and supporting infrastructure remains a major limitation for lunar missions, particularly at low altitudes where strong gravitational perturbations significantly affect spacecraft trajectories.

Several autonomous navigation strategies have been proposed for lunar operations, including relative navigation between spacecraft and image-based navigation using optical measurements. These approaches have demonstrated position accuracies on the order of tens to hundreds of meters in high-altitude lunar orbits, while optical techniques have shown particularly strong performance in landing and descent scenarios. However, each method presents inherent limitations. Relative navigation is constrained by spacecraft geometry and attitude requirements, while optical navigation is limited by lighting conditions, line-of-sight availability, and surface visibility.

This study investigates the use of gravity gradiometers for low-altitude navigation over the lunar surface. Gravity gradiometers measure the spatial derivatives of the gravitational acceleration, enabling the spacecraft position to be inferred using existing models of the Moon’s gravity field. Emerging next-generation instruments based on quantum interferometry and micro-electrostatic technologies promise improved stability and reduced size, mass, and power requirements, making gravity-gradient-based navigation increasingly viable for cislunar missions. The magnitude of the gravity gradient increases significantly at low altitudes, enhancing the information content of the measurements. At the same time, navigation performance becomes increasingly sensitive to uncertainties in spacecraft attitude, lunar orientation, and gravity field modeling, making accurate navigation particularly challenging. Moreover, current gradiometer technologies remain limited by long-term signal stability, which has been shown to be a primary limiting factor for gravity-gradient-based navigation in Earth-orbiting applications.

In this work, we present error-mitigation strategies that enable reliable navigation at altitudes between 50 and 100 km by analyzing the sensitivity of gravity gradiometer measurements to these errors. We assess the required levels of signal stability and measurement accuracy as a function of altitude. In addition, we investigate how navigation performance varies across different lunar regions and analyze how distinct features of the Moon’s gravity field contribute to the available navigation information. The proposed approach is demonstrated through high-fidelity simulations of gravity gradiometer measurements along a Lunar Reconnaissance Orbiter (LRO) trajectory, followed by an orbit determination analysis. These results provide guidance for the design of next-generation gravity gradiometers and future lunar mission concepts.
Initial results based on a consider-covariance analysis show that, under idealized conditions—assuming state-of-the-art gravity gradiometer performance with a noise level of approximately 10^-12 s^-2 (milli-Eotvos), perfectly stable measurements, and no unmodeled dynamics—the spacecraft position uncertainty can be reduced to the meter level at an altitude of 50 km. At this altitude, the assumed instrument accuracy enables sensitivity to gravity-field features up to spherical harmonic degree and order 100, as characterized by the GRGM1200A lunar gravity model. These findings underscore the need for computationally efficient onboard gravity-field evaluation techniques. In response, two navigation approaches are compared: recursive, dynamics-driven filtering methods such as the extended Kalman filter, and gravity-based localization through matching local gravity anomaly measurements against a prior reference map.