## 1 Introduction  
Almost 5250 launches have been made since the first artificial satellite, Sputnik, was launched on 4 October 1957 by the Soviet Union, leading to almost 42,000 objects in orbit [1]. Out of these, as of 22 July 2020, 20,987 objects are constantly being tracked by the Space Surveillance Network of the United States and among them around 1300 have the capability of performing orbital maneuvers [2]. Detecting these maneuvers is crucial to maintain a catalog of these objects. In addition to helping maintain a catalog, these orbital maneuvers are a key element in space situational awareness, specifically in small-scale maneuvers that cannot be detected by traditional methods [3]. Maneuver detection also has to be performed to monitor active satellites and space debris, look for space events, and ensure that low Earth orbit satellites adhere to the deorbiting standards. By analyzing the maneuvers of a satellite, the purpose of orbital control can be determined. An important requirement in space situational awareness involves ascertaining the current locations of all the known space satellites [4; 5; 6; 7]. The space surveillance network, along with a space-based surveillance satellite, has a total of 29 space surveillance sensors, which are placed at strategic locations worldwide, to maintain a space object catalog that contains the current Keplerian elements of these objects in space [8; 9]. Many modern applications in industry and the military rely on this space catalog to perform various tasks, such as re-entry predictions, collision avoidance, drag make-up, space weather, orbit estimation, and deorbiting [10; 11]. Substantial research has been conducted on maneuver detection. [PERSON] developed a data processing algorithm to detect space collisions and satellite maneuvers [12]. In this method state vector parameters for tracked objects are placed in a catalog with the corresponding time. These time-dependent data can be processed to reveal sudden unexpected changesin parameter values referred to as space events. [PERSON] et al. analyzed the semimajor axis to predict the mode of maneuver on the basis of the anomalies found in it [13]. [PERSON] and [PERSON] developed a method using a low-pass orbit element filter, and the method proved to be an efficient technique to identify a potential cross-tagged or similarly degraded orbit solution accuracy event [14]. [PERSON] et al. developed an algorithm by calculating the span between two state distributions evolving from nonidentical uncorrelated tracks to detect spacecraft anomalies [15]. A study was conducted by [PERSON] and [PERSON] to detect the maneuvers on the basis of the event representation approach by using 14 thrust Fourier coefficients to identify the change point of unknown accelerations [16]. Satellite maneuver prediction using two-line element sets has been studied for several years, and various approaches have claimed success so far. One such study was conducted by [PERSON] et al., who proposed two novel methods to predict maneuvers using the two-line element (TLE) sets of a satellite [17]. The first method checks for certain anomalies of satellites between arbitrary element sets. The subsequent algorithm works by checking for unexpected changes by analyzing the robustness, harmonics, and statistics using the time series. Both algorithms work only on low Earth orbit (LEO) satellites and have achieved perfect results [18]. [PERSON] et al. introduced a trial-and-error method by finding the huge difference in the subsequent observation of a satellite's semimajor axis and by setting a threshold filter for a first-order polynomial with a window length between the successive data points through analysis of a satellite's TLE data. Surprisingly, the results have a 95% detection rate for all the maneuvers down to change in velocity at magnitudes less than cm/s [19]. [PERSON] et al.