This work presents a 6-DOF relative navigation filter architecture for autonomous rendezvous in cislunar space with an unknown and uncooperative target. The key innovation lies in modeling all dynamics and measurements in the chaser’s body-fixed frame, enabling full-state estimation without requiring any knowledge of the target. The filter combines an Extended Kalman Filter (EKF) for translation and a Multiplicative EKF (MEKF) for attitude, integrating heterogeneous sensor data across different rendezvous phases. Despite operating under strong non-linearities, environmental perturbations, and sensor transitions, the filters ensure accurate and continuous estimation, supporting closed-loop control throughout the entire maneuver.
Pilone et al. (2025) studied this question.