Simulation Study for Precise Orbit Determination of a Callisto Orbiter and Geodetic Parameter Recovery

Abstract

Callisto, the outermost of the four Galilean satellites, is identified as a key body to answer present questions about the origin and the formation of the Jovian system. Callisto appears to be the least differentiated and the geologically least evolved of the Galilean satellites, and therefore the one best reflecting the early ages of the Jovian system.While the ESA JUICE mission plans several flybys of Callisto, an orbiter would allow it to measure geodetic parameters to much higher resolution, as it was suggested by several recent mission proposals,e.g., the Tianwen-4 (China National Space Administration) and MAGIC (Magnetics, Altimetry, Gravity, and Imaging of Callisto) proposals. Recovering parameters such as those describing Callisto’s gravity field, its tidal Love numbers, and its orientation in space would help to significantly constrain Callisto’s interior structure models, including the characterization of a potential subsurface ocean.We perform a closed-loop simulation of spacecraft tracking, altimetry, and accelerometer data of a high inclination, low altitude orbiter, which we then use for the recovery of its precise orbit and of Callisto’s geodetic parameters. We compare our sensitivity and uncertainty results to previous covariance analyses. We estimate geodetic parameters, such as gravity field, rotation, and orientation parameters and the k2 tidal Love number, based on radio tracking (2-way Doppler) residuals. We consider several ways to mitigate the mismodeling of non-gravitational accelerations, such as using empirical accelerations and pseudo stochastic pulses, and we evaluate the benefits of an on-board accelerometer.We also investigate the added value of laser altimeter measurements to enable the use of altimetry crossovers to improve orbit determination and gravity-related geodetic parameters, but also to estimate the recovery of surface tidal variations (via the h2 Love number). For our closed-loop analyses, we use both a development version of the Bernese GNSS Software and the open-source pyXover software.