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CubeSats are low-cost platforms to test innovative technologies in-flight that also allow performing science [1].Adopting CubeSats is appealing due to the inherent savings in the design, production, and launch costs. While these costs scale with the mass, power, and size of the platforms, those related to operations do not obey the same trend. Navigation is among those operations that are performed routinely, regardless of the mission phase. Deep-space probes are usually navigated through ground-based radiometric tracking and orbit determination [2], which contribute significantly to the ground segment costs. Granting autonomous on-board navigation is instrumental to reducing the overall mission cost of small probes, and to pave the way for novel deep-space mission concepts [3].The Lunar Meteoroid Impacts Observer, or LUMIO, is a 12U CubeSat mission to observe, quantify, and characterize the meteoroid impact flux by detecting their impact flashes on the lunar far-side [4]. LUMIO was awarded ex-aequo winner of the European Space Agency's challenge LUCE (Lunar CubeSat for Exploration), under the SysNova framework § . An independent assessment conducted at ESA's Concurrent Design Facility (CDF) has proven the mission feasible ¶ , and as such LUMIO is currently being considered for future implementation. LUMIO is placed in a quasi-halo orbit about Earth-Moon L 2 [5], as shown in Figure 1a-1b. From this orbit, full-disk images of the lunar farside are acquired through the LUMIO-Cam, an optical instrument capable of detecting meteoroid impact flashes in the visible spectrum. As a secondary application of the scientific investigation, the LUMIO-Cam is used to navigate autonomously, employing the Moon full-disk images to perform horizon-based navigation [6]. This technique resembles the backup navigation in Exploration Mission 1 [7]. The spacecraft state is estimated without relying on ground station tracking. Demonstrating this capability is the major technological objective of LUMIO. This note describes the methods and tools developed to quantify the performances of the full-disk optical navigation in LUMIO. The horizon-based method [8] has been embedded into a simulator that generates synthetic Moon images via a ray tracer software. The position vector is then filtered to estimate the whole spacecraft state. Errors in the state, dynamics, and maneuver execution have been considered. Preliminary results show feasibility of the devised concept of operation for autonomous navigation and station keeping: requirements on position and velocity knowledge are satisfied.

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Deep-space optical navigation is among the most promising techniques to autonomously estimate the position of a spacecraft in deep space.The method relies on the acquisition of the line-of-sight directions to a number of navigation beacons. The position knowledge depends upon the tracked objects. This paper elaborates on the impact of the observation geometry to the overall performances of the method. A covariance analysis is carried out considering beacons geometry as well as pointing and input errors. A performance index is formulated, and criteria for an optimal beacons selection are derived in a scenario involving two measurements. A test case introducing ten available beacons pairs is used to prove the effectiveness of the developed strategy in selecting the optimal pair, which leads to the smallest achievable error.