LEVERAGING LIGHT-CURVE INVERSION FOR REAL-TIME KINEMATIC STATE ESTIMATION OF UNCOOPERATIVE TARGETS

The growing number of space objects threatens mission sustainability, making precise real-time tracking essential for Active Debris Removal (ADR) and In-Orbit Servicing (IOS) missions. For an uncooperative target, a Vision Based Navigation (VBN) relative pose (attitude and position) estimation system coupled with a state estimator are likely required. Missions can further be supported by commissioning unresolved observations of the target to produce light curves which can then be used to extract rotation rates and axes of rotation. This work performs the novel task of exploiting the light curves as kinematic priors to enhance the performance of the state estimator. An Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF) are implemented. Light curve motion prior extraction and VBN pose estimation are simulated. Three independent studies are performed exploiting the motion priors: (1) Optimizing the Kalman filter tuning for specific kinematic scenarios; (2) Injecting the priors as an initial condition to improve convergence and steady state errors; and (3) Enhancing an outlier rejection function with supplementary proxy measurements from the priors. Performance is evaluated on a custom synthetic light curve dataset based on the Atlas Centaur rocket body, and a private commercial dataset based on the Vega Secondary Payload Adapter from commercial collaborator, Clearspace. Pose estimation results are simulated based on state-of-the-art machine learning spacecraft pose estimators. By exploiting kinematic priors, convergence time and steady state error reductions of 3× or more are exhibited for certain state components, dependent on the kinematic scenario and filter tuning. In general, several trade-offs are observed with kinematic priors providing the opportunity for the lowest steady state errors. This method has the potential to improve the pose estimation accuracy for proximity operations of uncooperative tumbling objects, supporting ADR and IOS missions, especially considering the mild assumptions required.