Combination of altimetry crossover and Doppler observables for Precise Orbit Determination of a Callisto Orbiter and Geodetic Parameter Recovery

Callisto is identified as a key body to answer present questions about the origin and the formation of the Jovian system. The outermost of the four Galilean satellites appears to be the least differentiated and the least geologically evolved of the Galilean satellites, and therefore the one best reflecting the early ages of the Jovian system.  While the ESA JUICE mission will perform 21 flybys of Callisto, an orbiter would allow 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. By analyzing a combination of altimetry crossovers and radio tracking (2-way Doppler) observations, we estimate Callisto’s gravity field and orientation parameters, as well as its tidal Love numbers k2 and h2. We use Variance Component Estimation to derive optimal weights for the different observation types, and for parameter constraints. We compare our results for different orbital configurations to Doppler-based solutions to investigate the added value of laser altimetry measurements and we discuss our findings, e.g., that altimetry helps reducing correlations between orbit parameters and improving the estimation of orientation parameters. For our closed-loop analyses, we use both a development version of the Bernese GNSS Software and the open-source pyXover software.