Volume 43 Issue 4
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(4): 768-779 Open Access
GAO Lijing, CHEN Zhimin, GUO Guohang, WANG Chunmei. Recognition of Working Pattern of Space Science Satellite Based on Ensemble Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(4): 768-779 doi: 10.11728/cjss2023.04.20220301022
Citation: GAO Lijing, CHEN Zhimin, GUO Guohang, WANG Chunmei. Recognition of Working Pattern of Space Science Satellite Based on Ensemble Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(4): 768-779 doi: 10.11728/cjss2023.04.20220301022
shu

Recognition of Working Pattern of Space Science Satellite Based on Ensemble Learning

doi: 10.11728/cjss2023.04.20220301022 cstr: 32142.14.cjss2023.04.20220301022
  • 1.

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

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2022-03-01
  • Rev Recd Date: 2022-10-11
    • Available Online: 2023-06-19
    Abstract
  • Aiming at the issues of space science satellite telemetry parameters, such as large amount of data, high dimension, the need of numerous artificial resource consumption for preset massive thresholds, the preset thresholds that may not be applicable, and the current monitoring methods with low scalability, a working pattern recognition method is proposed for scientific satellite based on ensemble learning. Correlation coefficient statistical characteristics and mutual information theory are used to screen and reduce the dimension of telemetry parameter data. Data resampling technology is used to solve the problem of category imbalance for the dataset. An integrated learning model is used to identify the working mode of space science satellite. The method is verified with the real telemetry parameter data of quantum science satellites. And the algorithm model can be constructed in a short time, and the overall recognition accuracy rate reaches 99.67%, which can correctly identify the majority and minority class samples. The method can provide decision-making basis for ground personnel to judge the working mode of space science satellites.

     

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