A satellite anomaly detection method based on distance correlation coefficient and GPR model

被引：0
|
作者：
Sun Y. [1 ,2 ]
Li G. [1 ,2 ,3 ]
Zhang G. [1 ,2 ]
机构：
[1] Innovation Academy for Microsatellite of CAS, Shanghai
[2] University of Chinese Academy of Sciences, Beijing
[3] School of Information Science and Technology, ShanghaiTech University, Shanghai
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 04期
关键词：
Distance correlation coefficient; Gaussian Process Regression (GPR); Generalization error; Satellite anomaly detection; Variable selection;
D O I：
10.13700/j.bh.1001-5965.2020.0041
中图分类号：
学科分类号：
摘要：
During the orbital operation of the satellite, the telemetry data is usually represented by multidimensional time series. The Gaussian Process Regression (GPR) model can provide dynamic thresholds for important telemetry parameters and timely discover failure symptoms hidden within the engineering threshold. However, high dimensional satellite data makes GPR model limited. Therefore, in order to obtain the dynamic threshold related to multiple telemetry parameters, based on the GPR model, the distance correlation coefficient is combined to select predictive variables, reduce the information redundancy and the amount of calculation, and improve the interpretability of the model.The generalization error of the model is estimated to set a more reasonable prediction interval, to improve the generalization ability and detect the continuous abnormality of the data stream. Simulation experiments on actual orbiting satellite data verify that this method can detect data anomalies in the early failure of the satellite, improve the prediction performance of the model and reduce the false alarm rate. © 2021, Editorial Board of JBUAA. All right reserved.
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页码：844 / 852
页数：8
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