## 1 Introduction
suggested a technique to detect a satellite's maneuvers by comparing the establishment state of a certain orbital parameter with the probability error prediction, which is fitted using sample data generated by a polynomial fit from the TLE sets of a satellite [20]. [PERSON] et al. proposed an algorithm to detect orbital maneuvers by using unsupervised classification methods, such as K-means, hierarchical, and fuzzy C-means clustering, through data mining of the TLEs of the satellite [21]. [PERSON] and [PERSON] proposed a series of least-squares differential correction fitting and propagation methods to detect a satellite's maneuver on the basis of the TLEs of the satellite. Their research also consisted of a viable collision avoidance system from space debris [22]. [PERSON] and [PERSON] implemented parallel processing by fusing TLE and state propagation methods to detect maneuvers, thereby increasing the computation speed and accuracy of the existing technique by factors of eight and four, respectively [23]. [PERSON] improved the accuracy of the long-term prediction of orbital elements by using the least-squares method, which deduces a fit for long sets of TLE data by a semianalytic propagator [24]. At any given point in time, TLE is a representation of the orbital parameters of an object orbiting Earth [17]. Given that TLEs are updated on a regular basis, they comprise vital information, such as the perturbation effects (environmental and non-environmental) of the orbit. TLEs have been used in several applications, which derive all the information from the TLE catalog [25; 26; 27]. One specific use of TLEs is to advance the atmospheric density corrections pioneered by [PERSON] et al. [28]. However, the interest of this study was to detect the maneuver history of Earth's orbiting satellites using TLE datasets. As the name suggests, TLE data consist of two lines comprising a series of orbital elements of a given object orbiting Earth at a specific instant in time, sometimes with a preceding title line. Simplified perturbation models are used to predict the orbital position and velocity vectors of a particular satellite or space debris at any given point in time [29; 30; 31]. In total, five sets of prediction models are used: simplified general perturbations (SGP), SGP4, simplified deep space perturbations (SDP4), SGP8, and SDP8 [32; 33]. However, SGP4 and SDP4 are the most used models, depending on the altitude of the considered satellite [34]. These five models predict the state vector of a satellite in consideration of the effects of the perturbations that are caused by the shape, atmospheric drag, and radiation pressure of Earth, as well as the gravitation caused by different celestial objects, including the Sun and the Moon [35; 36; 37]. TLEs are generated only for Earth-orbiting objects, and the format of the TLE sets is distinct to each simplified perturbation model. Therefore, not just any model can be used for any TLE dataset; one must implement the specific format of TLEs for specific simplified perturbation models. However, all these methods can be used to detect a satellite's maneuvers only if substantial operational data are available. Detecting the maneuvers for all active objects that do not have any operational information or very little maneuver information can be difficult. Nevertheless, the same maneuvers can be easily detected by using historical TLE data. Maneuvers must be detected in real-time to provide enough time to analyze spacecraft anomalies and clear a suspected threat to a nearby spacecraft or debris. The maneuvers of a satellite can be detected by following certain patterns or trends from recorded data such as maneuver histories, semi-major axis, eccentricities, etc. of active satellites [38]. Such information is vital because it can be used to predict or foresee and analyze a possible maneuver in the future; it can also be provided to a tracking algorithm that allows real-time maneuver detection and analysis.