Optimal Beacons Selection for Deep-Space Optical Navigation
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Optimal Beacons Selection for Deep-Space Optical Navigation V. Franzese1
· F. Topputo1
Accepted: 21 October 2020 © American Astronautical Society 2020
Abstract 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. Keywords Autonomous navigation · Optimal beacons selection
Introduction The state of the art for spacecraft navigation is radiometric tracking [24]. The accuracy of the orbit determination solution lies in the order of meters in near-Earth environment whereas it is in the order of kilometers in deep space. This method yields the lowest error achievable with current technology, though it requires persistent contact with ground. Moreover, in two-way communication the orbit determination is performed on ground as well. All in all, radiometric-based orbit determination involves a consistent allocation of resources and assets.
V. Franzese
[email protected] F. Topputo [email protected] 1
Department of Aerospace Science and Technology, Politecnico di Milano, Via La Masa 34, 20156, Milan, Italy
The Journal of the Astronautical Sciences
Automation is required for next-generation missions [19]. There is an incoming wave of miniaturized interplanetary probes [18]: the European Space Agency (ESA) has funded several interplanetary CubeSat mission studies like M-ARGO (Miniaturized Asteroid Remote Geophysical Observer) [26], LUMIO (Lunar Meteoroid Impacts Observer) [7, 22, 25], VMMO (Lunar Volatile and Mineralogy Mapping) [12], and CubeSats along the Hera mission [14]; the National Aeronautics and Space Administration (NASA) funded 19 SmallSat deep-space mission studies after Mars Cube One (MarCO) [11], the first interplanetary CubeSat launched along with InSight (Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport) mission. Interplanetary CubeSats missions are on the verge of becoming prosperous. Yet, their overall cost scales with the system mass, except for operations [26]. There is therefore the need to reduce operation costs, in particular for what concerns flightrelated operations, which are performed incessantly during the entire lifetime. In the field of navigation, the key is to enable autonomous positioning by inferring information from the surrounding environment. Autonomous navigation
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