## 1 Introduction  
Resident Space Objects (RSOs) are man-made objects orbiting the Earth, including satellites, space stations, and space debris. These objects remain in space for long periods, posing a significant threat to currently operational space assets [1]. To reduce the risk of on-orbit collisions, spacecraft operators must improve situational awareness of potential RSO threats, especially non-cooperative targets, including non-cooperative satellites and space debris. With the gradual application of electric propulsion technology to all kinds of space missions [2, 3, 4, 5], such as the deployment, orbit maintenance, and de-orbiting operation of Starlink and other giant low-orbit constellations, the number of satellites maneuvering in space is increasing, which also increases the difficulty of situational awareness. Currently, RSO monitoring and cataloging methods include ground-based observations and Space-Based Space Surveillance (SBDS). Spaceborne sensors have high accuracy, wide fields of view, and are less affected by weather conditions. Various techniques, including multiple hypothesis methods [5], genetic algorithms [6], and various estimation methods such as batch estimators [7] and sequential estimators [2; 3; 4; 5; 6; 7; 8; 9], as well as data fusion methods [1], are applied in SBSS. However, using spaceborne angle-only measurements for precise orbit estimation is a major challenge. The inherent field-of-view limitations of spaceborne Electro-Optical Sensors (EOSs) results in RSOs being observable only over very short arcs, often producing inaccurate results. Ground-based observations are a more stable monitoring method. There are many methods for the orbit determination of maneuvering targets based on ground observations, which can be broadly divided into three categories: orbit determination process restart, filtering methods, and maneuver reconstruction. The orbit determination process restart method does not consider the pre-maneuver orbit and instead reinitializes the post-maneuver orbit by directly increasing the filter's uncertainty (covariance). Although this method is simple, it cannot model the maneuver process. Unlike with non-motorized satellites, there is uncertainty in the dynamics model of the orbit determination problem of motorized satellites. If the unknown maneuvers are not correctly compensated, the orbiting accuracy will be seriously affected. Kalman filtering algorithms are widely used in the orbit determination of maneuvering targets. These commonly used filtering methods can be further classified into three categories: single model-based adaptive Kalman filtering algorithms [10], multi modeling algorithms [11], and decision-based adaptive Kalman filtering algorithms [12]. [PERSON] [10] address the GEO satellite station maintenance problem by defining a new control index for limited thrust transfer, establishing a permissible control region for maneuver detection, and then proposing a particle filtering-based tracking method for low-orbit surveillance radar trajectory tracking, which maintains surveillance of the target under data-scarcity conditions even if it is observed only by the surveillance radar under prolonged continuous thrust maneuvers. [PERSON] et al. [11] establish an eighth-order polynomial to model the unknown maneuver, and estimate the polynomial coefficients using a Long Short-Term Memory (LSTM) neural network. This does not rely on good maneuver detection and initial values of the maneuver parameters, and makes up for the shortcomings of the traditional adaptive Kalman filtering algorithm with a single model. [PERSON] [12] proposed an improved thrust Fourier coefficient model using the orbit averaging method. They also proposed a robust optimization algorithm based on the extended Kalman filtering method combining IDE and equivalent noise to improve the orbit setting accuracy.