Volume 40 Issue 1
Jan.  2020
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LUO Zhongkai, LI Hu, HU Tai. Optical Experiments Prediction of the Quantum Science Experiment Satellite Based on Gradient Boosting Decision Tree[J]. Chinese Journal of Space Science, 2020, 40(1): 126-133. doi: 10.11728/cjss2020.01.126
Citation: LUO Zhongkai, LI Hu, HU Tai. Optical Experiments Prediction of the Quantum Science Experiment Satellite Based on Gradient Boosting Decision Tree[J]. Chinese Journal of Space Science, 2020, 40(1): 126-133. doi: 10.11728/cjss2020.01.126

Optical Experiments Prediction of the Quantum Science Experiment Satellite Based on Gradient Boosting Decision Tree

doi: 10.11728/cjss2020.01.126
  • Received Date: 2018-12-10
  • Rev Recd Date: 2019-06-25
  • Publish Date: 2020-01-15
  • The quantum science experimental satellite mainly carry out four kinds of optical experiments during the orbital operation. The ground monitoring personnel mainly judged whether the satellite carried out optical experiments, experimental types and experimental results through the telemetry parameter threshold. This method requires a large number of thresholds to be set in advance, which requires a lot of manpower, and these thresholds need to be reset according to the on-orbit satellite, and the scalability is poor. Aiming at the above problems, this paper proposes an optical experiment discriminating method based on machine learning. Firstly, the optical experiment monitoring task of quantum science experimental satellite is abstracted into a multi-classification problem in machine learning. A classification model is constructed, and then the quantum science experimental satellite is used. The real historical telemetry data is used to train the model, and finally the trained model is verified by the real experimental plan. The experimental results show that the proposed method can achieve 99% accurate accuracy without the expert prior knowledge, and can be used for real-time monitoring tasks of quantum science experimental satellite optical experiments. The machine learning-based discriminant method proposed in this paper has strong scalability and can be widely extended to other monitoring tasks of satellite orbit operation.

     

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