# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 1 Introduction  
Space objects in the Earth's orbit include spacecraft, rocket debris, and space debris. Their number has grown exponentially due to increased space activities, exacerbating orbital congestion and collision risks. The number of on-orbit objects is currently 44,700 [1]. Most are uncontrolled space debris that pose a significant risk to the operational safety of on-orbit spacecraft [1, 2]. Therefore, it is necessary to monitor the location, characteristics, functions, and space environment of all objects in Earth's orbit using space surveillance systems to improve space situational awareness [3]. The process of cataloging space objects plays a vital role in achieving this goal. However, data acquisition is costly, and the observation equipment has limited capacity. Thus, the space objects catalog may contain outliers due to space events, such as solar activity, geomagnetic variations, collisions, or orbital maneuvers [4]. These outliers represent orbital elements that deviate significantly from the actual values, which undermines the credibility and reliability of subsequent analytical procedures. Therefore, the timely and accurate detection of TLE outliers and space events is crucial to ensure their operational safety. The two-line element (TLE) catalog created by the North American Aerospace Defense Command (NORAD) is the most complete catalog of orbital information for describing the positions and velocities of space targets. Due to its provision of orbital information, TLEplays a vital role in satellite orbit prediction, collision avoidance, orbit tracking, and other fields. However, TLE data carry inherent uncertainties and errors arising from factors such as the quality of the observation data, loss of computational accuracy, and simplifications in models. Moreover, NORAD do not furnish information regarding their accuracy; thus, directly utilizing TLE data may pose potential risks or compromise the stability and reliability of subsequent research outcomes. Two TLE outlier and space event detection methods using historical TLE data have been used. The first method utilizes the semi-major axis or inclination for maneuver detection. For example, several researchers [5; 6; 7; 8] used a moving window to fit the semi-major axis and predict future movements. This approach averages the data in the window. The results depend on the window size, which is difficult to determine due to the irregularity of the TLE release time. Some researchers have detected maneuvers by assuming that the semi-major axis of the target is highly variable [9; 10]. The second object detection method is an algorithm based on a probabilistic statistical approach. It does not determine if the target orbit is stable but evaluates whether the TLE data are consistent with the target's predicted movement [11]. References [12; 13] conducted maneuver detection experiments assuming a Gaussian distribution and using discriminative methods, such as the Euclidean or Mahalanobis distance. However, due to large fluctuations in the TLE's accuracy, it is difficult to detect low-thrust satellite maneuvers [14]. Machine learning algorithms are increasingly used for TLE outlier and maneuver detection as computer hardware capabilities have improved. These methods can learn the \"long-term orbital motion patterns\" of a target from a series of historical TLE data and use them as a basis for identifying anomalies and space events. These machine learning methods can be roughly divided into clustering discrimination and reconstruction discrimination according to the criteria of anomaly judgement. Clustering techniques such as Gaussian mixture models, K-nearest neighbour methods, multilayer neural networks, and convolutional neural networks, among others, identify points that are distant from the cluster centre in the data set as anomalies [15; 16; 17].