Robust Space-Object Association and Recursive Estimation for Ground-Based Optical Observation Under Degraded Orbital Priors and Site-Position Uncertainty

Ground-based optical observation is a key sensing modality for space-object monitoring, but reliable association becomes challenging when orbital priors are degraded, site-position information is imprecise, and each frame contains multiple point-source candidates. This paper proposes a robust framework for single-target association among multiple image candidates and recursive estimation under such conditions. The method first converts image-domain candidates into unit line-of-sight (LOS) directions and represents their local deviations in a prediction-aligned tangent plane. Angular measurement noise and site-position uncertainty are then propagated into the local covariance model. Based on this representation, a kinematic-photometric five-dimensional (5D) normalized innovation squared (NIS) gating statistic is constructed by jointly evaluating local position, pseudo-velocity, and photometric consistency. After association, a three-dimensional decoupled Kalman update is performed using only the single-frame position and photometric measurements. Experiments on four real ground-based optical satellite observation sequences, including two static scenarios and two dynamic scenarios, show correct association rates of 100.00%, 100.00%, 100.00%, and 85.86%, respectively. These results demonstrate that the proposed framework improves association reliability under degraded orbital priors and imprecise site-position information while maintaining stable recursive estimation.