# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 3 Results and Discussion
Figure 3: 27386 semi-major axis and change rate plot. The 95 th quantile was then utilized to establish the anomaly detection threshold, and the sample data sequence was processed to identify outliers. The outcomes of the outlier detection are illustrated in Figure 6. For clarity, we define an 'outlier' or 'anomaly' based on the magnitude of the deviation between adjacent measurements rather than predicted errors. Specifically, deviations in orbit inclination greater than 0.003\({}^{\circ}\) and semi-major axis deviations approximately exceeding 20 m are considered significant. These thresholds are not exact figures but rather represent a scale of detectable anomalies, as our anomaly score is a composite index derived from the loss function values of these two variables. It is, therefore, not feasible to provide an exact size for any single variable. This approach ensures that our method can detect TLE outliers of a significant size that may not be identifiable by simpler methods. It is evident that there is a notable presence of outliers in the TLE when there are frequent changes in the orbital elements of the cataloguing data. Therefore, it becomes imperative to enhance responsiveness through the robust correction function, consequently refining the accuracy of outlier detection and thereby bolstering the detection of historical maneuvers. Building upon the results of the outlier detection, the maneuver detection process is employed to detect historical maneuver events. In order to assess the efficacy of the proposed method for the detection of maneuvers, the Receiver Operating Characteristic (ROC) plots for both the MGMMMGA and GMMEM methods are presented in Figure 7. It should be noted that the GMMEM is a Gaussian Mixture Model (GMM) obtained using the Expectation Maximization (EM) algorithm. The ROC plot of the MGMMMGA method was observed to be closer to the upper left corner, indicating that our model outperforms the GMMEM method. In terms of the area under the curve (AUC), a higher AUC indicates superior performance in maneuver detection. The results showed that the area under the curve of the MGMMMGA method was 10% higher than that of the GMMEM, which can effectively improve the maneuver detection performance and lay a solid groundwork for understanding subsequent space target motion patterns and orbits. The aforementioned process is iterated for the remaining targets to derive spatial event detection outcomes for all targets. These results are summarized in Table 2. From Table 2, it is clear that the MGMMMGA recall and F1 scores outperform those of the GMM model. In terms of precision, for targets 27386, 41335, and 39086, the GMM model outperforms the MGMMMGA model. This is due to the specific maneuvering characteristics of these targets. Their maneuvering events have extremely short durations, typically less than 1 s. These ultra-low-thrust maneuvers are prone to misjudgment, which may result in the lower accuracy of the MGMMMGA model. In contrast, the GMM model has a broader fitting range, which is only applicable when forecasting error data for larger thrusts. Furthermore, it is insensitive to low-thrust data. This explains why, out of the eight targets, only 27386, 41335, and 39086 have a higher detection accuracy with GMM compared to MGMMMGA. This observation suggests that, in case of large fluctuations in TLE precision, the down  
Figure 5: Quantile–quantile plot (Comparison between the sample distribution, shown in blue, and the theoretical distribution, shown in red.). weighting treatment of suspicious data can improve the GMM model's performance for space event detection in the scenario of contaminated TLE data. However, outlier and space event detection for different targets requires further investigation. Our methodology does indeed rely on the prediction error characteristics