An open-source multi-mission, multi-observation estimation tool for natural satellites&#8217; dynamics - Application to Jupiter&#8217;s Galilean moons
Authors: Fayolle Marie , Dirkx Dominic , Garrett Geoffrey , Gurvits Leonid I. , Hener Jonas , Lainey Valery , Magnanini Andrea , Visser Pieter 
Published: March 2022

&lt;p&gt;&lt;strong&gt;Context&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;When generating ephemerides of natural satellites, available tracking data from different space missions, and/or Earth-based photo-/astrometric observations, are not systematically combined in the estimation. Exploiting the complementarity between different data types and data sets is however a key possibility for improving current solutions [1]. In the near future, this will be particularly crucial for Jupiter&amp;#8217;s Galilean moons: the synergy between past and upcoming missions (&lt;em&gt;e.g.&lt;/em&gt; Galileo, Juno, JUICE, Europa Clipper) and Earth-based observations is critical to better determine their strongly coupled dynamics [2,3].&lt;/p&gt;&lt;p&gt;To facilitate such moons&amp;#8217; ephemerides analyses, we are developing a multi-mission, multi-observation estimation tool. This simulation tool is part of the Tudat(py) open-source software (Python/C++ interfaces, C++ back-end), developed by TU Delft&amp;#8217;s Astrodynamics &amp; Space Missions department [4].&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Estimation tool capabilities &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Our estimation tool can simulate multiple missions and various observation types. Regarding space missions, any number of spacecraft can be included in the estimation, around any natural body. The simulator typically takes SPICE kernels as inputs for the spacecraft&amp;#8217;s trajectories [5]. Any change in mission design can therefore be easily investigated by updating the kernel of interest.&lt;/p&gt;&lt;p&gt;The traditional radiometric observables are available (range, Doppler, VLBI), along with direct photo-/astrometry, either Earth- or spacecraft-based. In addition to the spacecraft's and natural bodies' states, various dynamical parameters are estimable, including gravity field coefficients, tidal dissipation parameters, as well as spacecraft- and mission-specific properties (empirical accelerations, observation biases, &lt;em&gt;etc.&lt;/em&gt;).&lt;/p&gt;&lt;p&gt;The entire estimation software is freely available to the community [4]. As such, it is directly usable and modifiable, also greatly facilitating verification work. An open-source simulator will be provided for the Galilean moons specifically.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Ongoing and future applications&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Regarding Galilean moons&amp;#8217; ephemerides, our software has already been used to compare different state estimation strategies, using JUICE tracking data only, as well as to study a novel approach to include mutual approximations in the estimation [6].&lt;/p&gt;&lt;p&gt;The upgraded multi-mission, multi-observation tool now allows to quantify the contribution of diverse data types and/or data sets. Furthermore, it provides the means to analyse the solution&amp;#8217;s sensitivity to spacecraft&amp;#8217;s trajectories, dynamical modelling choices, as well as to the observations&amp;#8217; quality and schedule. This is essential to determine which combination of data sets or which observations planning strategy would benefit the solution most.&lt;/p&gt;&lt;p&gt;We will first apply this multi-mission functionality to investigate the unique opportunity for concurrent in-system observations offered by JUICE and Europa Clipper. We will particularly focus on (PRIDE) VLBI data [2], including the possibility for VLBI measurements between the two spacecraft, which would provide valuable information about their relative angular position.&lt;/p&gt;&lt;p&gt;We will also extend our tool&amp;#8217;s current capabilities, implementing additional observable types to simulate more diverse Earth-based observations (radar, stellar occultations, mutual events, mutual approximations). This will allow us to assess their contribution to the solution, and thus to define priorities, for both observations planning and data merging.&lt;/p&gt;&lt;p&gt;[1] Lainey et al., 2020&lt;/p&gt;&lt;p&gt;[2] Dirkx et al., 2017&lt;/p&gt;&lt;p&gt;[3] Magnanini et al., in preparation&lt;/p&gt;&lt;p&gt;[4] &lt;em&gt;https://github.com/tudat-team/tudat-bundle&lt;/em&gt;&lt;/p&gt;&lt;p&gt;[5] Acton et al., 1996&lt;/p&gt;&lt;p&gt;[6] Fayolle et al., 2021&lt;/p&gt;