In response to the need for quasi-real-time and high-confidence research and judgment of spacecraft abnormal proximity symptoms in space situation awareness, this paper proposes an intelligent detection method for orbital anomalies based on the high-dimesional representation of spacecraft behavior. Based on the target orbit element database to extract the spacecraft behavior time series characteristics and generate a high-dimensional representation matrix, a convolutional neural network structure integrating multi-dimensional characteristic classification and detection is designed, the orbital abnormal behavior characteristics is automatically learned, and it’s detected whether the spacecraft orbital is abnormal. The historical Two-Line Elements (TLE) data are used to generate a test set of abnormal orbital behavior of the spacecraft, and the Mahalanobis distance method and the intelligent detection method are used to jointly detect the test set. The test results show that the intelligent detection method provides a better orbital anomaly detection success rate on the self-built test set than the Mahalanobis distance method, which is increased from 85% to 98.5%. The intelligent detection method can be effectively used for detection of abnormal spacecraft orbital behavior.


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    Title :

    Intelligent detection of spacecraft abnormal behavior based on feature dimensionality representation algorithm


    Contributors:

    Conference:

    Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023) ; 2023 ; Wuhan, China


    Published in:

    Proc. SPIE ; 12803


    Publication date :

    2023-10-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English