A machine learning method for the orbit state classification of large LEO constellation satellites

Wang, Dongfang; Li, Fen

doi:10.1016/j.asr.2022.09.040

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…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]. Reconstruction discrimination methods, such as principal components analysis, generative adversarial networks, and autoencoder networks, project the data set into a subspace and calculate the reconstruction error, judging samples that do not match the distribution of training samples as anomalies [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Zhang,

Zhao,

2024

Applied Sciences

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The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

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Section: Introductionmentioning

confidence: 99%

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

Zhang,

Zhao,

2024

Applied Sciences

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Resident space objects classification by machine learning techniques

Samorodova

2024

J. Phys.: Conf. Ser.

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In recent years the number of resident space objects in near-Earth space has been increasing drastically, which can lead to a “cascade collisions” effect and other catastrophic consequences. Therefore, it is crucial to monitor resident space objects, maintain and update catalogues on time, including the identification of unknown space objects to estimate their location and potential orbit. It is necessary to perform resident space object identification quickly and accurately to avoid possible satellite collisions resulting in large clouds of uncontrollable space debris fragments. That is why the task of resident space object preliminary classification by TLEs is stated and solved by applying machine learning approaches to achieve a higher classification quality and speed. This issue is a highly imbalanced classification problem, which narrows and specifies the used models. Applying the proposed resident space object classification model to space surveillance systems can decrease the time required for the object identification significantly.

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