## 1 Introduction
To address the challenge of initializing maneuver accelerations, [PERSON] et al. (2016) proposed an empirical formula based on the TLE B* parameter. This approach, validated on a large set of climbing Starlink satellites, was shown to significantly improve filter convergence. Nevertheless, our experiments reveal that this method no longer performs effectively for the newer generation of Starlink satellites, such as the V2 Mini version, indicating limited generalizability across evolving spacecraft platforms. With regard to noise modeling, accurate specification of process and measurement noise statistics is important to ensuring filter stability and estimation accuracy. [PERSON] et al. (2017) extended the concept of uncertainty propagation to the prediction of measurement uncertainty, thereby improving data association performance. However, the maneuvering case remains unexplored in their research. [PERSON] et al. (2017) investigated the impact of noise variance on Kalman filter performance and proposed using genetic algorithms (GA) to optimize both process and measurement noise variances. Their results showed that this optimization reduces estimation errors by avoiding arbitrary parameter selection. [PERSON] et al. (2017) incorporated the process noise covariance associated with the TFC model offset into an iterative extended Kalman filter (EKF), in order to compensate for the unmodeled dynamics and improve the OD accuracy. The limitation of this method is that the process noise uncertainty must be estimated offline in advance. For more general maneuvering scenarios, [PERSON] et al. (2017) developed a thrust estimation method based on ensemble Kalman updating. This approach avoids linearizing the spacecraft dynamics and relaxes Gaussian assumptions on parameter and measurement noise distributions. Despite its advantages, the method relies on prior knowledge of thrust activation and termination times, which are often unavailable for non-cooperative targets. Consideringthe unknown or time-varying measurement noise characteristics, [PERSON] and [PERSON] [29] proposed an adaptive noise factor approach based on the UKF, which dynamically adjusts the measurement noise covariance matrix. Although this approach allows the noise factor to adapt to changing conditions, an upper bound must still be manually defined for each specific case. To address the challenges of orbit determination for Starlink satellites--non-cooperative targets with unknown maneuvering parameters--this study proposes a dedicated framework. The focus is on the orbit-raising phase, where satellites perform frequent low-thrust maneuvers and observational data is sparse. The proposed framework employs a batch processing method combined with UT to estimate the satellite's state vectors (position and velocity) as well as the equivalent thrust acceleration at selected epochs. By making full use of available observations, the method improves the stability and accuracy of orbit determination. It avoids dependence on predefined process noise models by achieving convergence through iterative refinement. Moreover, the implementation of UT via deterministic sampling effectively addresses measurement-state nonlinearities. Unlike the conventional symmetric sampling strategy used in [30], this study adopts the rho-minimum sigma set strategy [31], which generates a reduced number of sigma points. This modification can significantly reduce computational cost while maintaining estimation performance in high-dimensional augmented state spaces where position, velocity, thrust acceleration, and perturbation-related parameters are estimated simultaneously. The following part of this paper begins by introducing a reduced dynamics model based on the orbital characteristics of Starlink satellites during the orbit-raising phase. Building on this, an unscented batch filtering method is developed for orbit determination, incorporating the rho-minimum sigma set sampling strategy to improve computational efficiency.