Volume 44 Issue 6
Dec.  2024
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Article Navigation >  Chinese Journal of Space Science  > 2024  >  44(6): 1155-1165 Open Access
ZHOU Taichun, GUO Guohang, XIAO Zhigang, LI Hu. Anomaly Detection of Satellite Telemetry Data Based on Latent Space Interpolation Autoencoder (in Chinese). Chinese Journal of Space Science, 2024, 44(6): 1155-1165 doi: 10.11728/cjss2024.06.2023-0147
Citation: ZHOU Taichun, GUO Guohang, XIAO Zhigang, LI Hu. Anomaly Detection of Satellite Telemetry Data Based on Latent Space Interpolation Autoencoder (in Chinese). Chinese Journal of Space Science, 2024, 44(6): 1155-1165 doi: 10.11728/cjss2024.06.2023-0147
shu

Anomaly Detection of Satellite Telemetry Data Based on Latent Space Interpolation Autoencoder

doi: 10.11728/cjss2024.06.2023-0147 cstr: 32142.14.cjss.2023-0147
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2023-12-13
  • Rev Recd Date: 2024-01-19
    • Available Online: 2024-11-02
    Abstract
  • Satellite telemetry parameters are the critical indicators for the ground operation and management system to assess the normal state of satellite operation in orbit, and anomaly detection of telemetry parameters is essential to guarantee the safe and reliable operation of satellites and the smooth execution of tasks. In response to the existing satellite telemetry anomaly detection algorithms for parameter feature extraction there is a lack of differentiation, effective anomaly decision-making information is not sufficiently extracted and other problems, this paper proposes an anomaly detection method based on the optimization of latent space interpolation, the latent space optimization constraints after the self-coder’s representation learning ability and the density estimation ability of the Kernel Density Estimation (KDE) method are combined to effectively carry out the anomaly detection. Real telemetry parameter data from quantum science satellites and public datasets are used for validation, and the results show that the proposed method improves the Auc and F1 values over the optimal comparison method by 5.6% and 5.8%, respectively, on real telemetry parameters. Compared with other anomaly detection algorithms, the proposed method has strong ability to discriminate normal and abnormal samples, effectively solves the problems of lack of differentiation of features and insufficient extraction of decision information, and has good noise immunity and effectiveness.

     

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