Interplanetary Navigator: Development and Validation through Optimal Control Trajectory Optimisation

This work extends the CNES MATLAB Navigator from Earth-centric to interplanetary operations by enriching its dynamics and measurement toolkit within an Extended Kalman Filter (EKF) that runs over “orbital windows” and is driven by modular inputs, directives, and telecommands. The dynamics now include IAU-compliant inertial and body-fixed frames (ICRF/BCF), TDB time handling, Chebyshev-based ephemerides, third-body gravity (including solid tides where relevant), first-order relativistic terms, a plate-based solar-radiation-pressure model with eclipse logic, and thrust profiles. On the navigation side, the EKF processes optical line-of-sight (LOS) angles and a LiDAR range model for proximity operations; a mission-generation tool in Java produces scenarios, measurements, and reference ephemerides allowing for testing of the navigator in different missions and environments. A campaign of tests demonstrates performance and limitations. Propagation accuracy is validated and LOS+LiDAR measurements allow for sub-millimetre state accuracy in planetary orbits. Along non-orbital trajectories, such as the Juventas trajectory (HERA), simplified mean-thrust modelling can induce large transient errors that the filter still re-converges when measurements are available. In deep-space cruise, LOS-only is insufficient, LiDAR is unavailable and long-range range/Doppler would be required. To have a case study in deep space, a guidance analysis allowed for the development of an interplanetary trajectory. The Optimal Control Theory has been studied and adapted on a Fortran trajectory optimizer to compute an optimal low-thrust transfer to the near-Earth asteroid 2000 SG344. The terminal flyby was then evaluated with the navigator, which achieved high accuracy in proximity. Overall, the navigator meets its goals for planetary and for some interplanetary missions. To achieve full interplanetary capability, future work should integrate long-range ranging (e.g., radio), refine thrust modelling, and implement attitude laws to fully couple SRP and sensing.