## 1 Introduction
Like other sequential estimation methods, the UKF updates the system state vector in real time through a prediction-correction cycle, where the prediction step relies on orbital dynamics and the correction step incorporates newly acquired measurements to refine the estimate. However, in the context of LEO space targets undergoing continuous low-thrust maneuvers, the interval between successive observation arcs may become prolonged--often exceeding ten hours. In such cases, the unknown low-thrust acceleration introduces significant model uncertainty, leading to diffusion of the state covariance matrix during propagation. This not only slows convergence but may also result in filter divergence [13]. For orbit determination of maneuvering space targets, a variety of methods have been developed based on variants of LSM and KF [14; 15; 16; 17; 18]. These approaches can be broadly classified into 3 categories: reinitialization of orbit determination, maneuver reconstruction, and filtering-based approach [17; 19; 20]. The reinitialization approach [21] uses only post-maneuver measurements and does not explicitly model the maneuver itself. While simple, it fails to account for maneuver dynamics. The maneuver reconstruction approach [17; 22; 23] seeks to estimate detailed maneuver parameters, such as thrust magnitude, direction, and timing (i.e., maneuver start and end times). Although this method can provide more physically accurate representations of the maneuvering behavior, the high computational cost limits its application to scenarios involving frequent maneuvers or multiple targets. Moreover, it is unsuitable for orbit prediction (OP) when future maneuver plans are unknown or unavailable. In contrast, the filtering-based approach, which is grounded in KF and its nonlinear variants, offers a more flexible framework for estimating and predicting the orbits of non-cooperative targets. This class of methods avoids the need for detailed maneuver profiles by incorporating an equivalent or compensated dynamics model. The dynamic system with unknown maneuvers can be modeled in different ways. To model the maneuvers of geostationary (GEO) satellites, [PERSON] et al. (2015) employed [PERSON]'s high-resolution differential formulation in combination with polynomial fitting. In their approach, the maneuver acceleration is treated as a residual term, obtained by subtracting the modeled acceleration from the interpolated total acceleration derived from the estimated satellite states. [PERSON] et al. (2016) adopted a polynomial representation model for unknown maneuvers and introduced a transformation technique that reduces sensitivity to higher-order temporal terms. This approach enhances the convergence and robustness of the OD process. Based on the observed orbital characteristics of climbing Starlink satellites, [PERSON] et al. (2016) adopted a model that assumes a constant but unknown acceleration over multiple days. They developed a recursive OD and OP framework to enable tracklet-to-object association. In the context of tracking maneuvering targets, [PERSON] et al. (2016) and [PERSON] et al. (2017) modeled the target maneuvers as a first-order Markov process. [PERSON] and [PERSON] (2016, 2017) proposed the Thrust-Fourier-Coefficient (TFC) model to represent the averaged variations in classical Keplerian elements caused by maneuvers. [PERSON] et al. (2017) further improved this model by replacing Keplerian elements with nonsingular orbital elements. Besides the maneuver modeling, the performance of the filtering-based OD methods largely depends on the estimation of the initial state and the characterization of noise statistics. In general, initial position and velocity are relatively straightforward to obtain. These can be derived from existing ephemeris data of cataloged satellites or computed through initial orbit determination (IOD) techniques based on early-stage observational data. In contrast, initializing unknown maneuver accelerations is significantly more challenging, especially in the absence of prior information.