Anomaly Detection of Satellite Telemetry Data Based on Latent Space Interpolation Autoencoder
Satellite telemetry parameters are the critical indicators for the ground operation and man-agement 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 pa-rameter feature extraction there is a lack of differentiation,effective anomaly decision-making informa-tion 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 re-sults show that the proposed method improves the Auc and Fl values over the optimal comparison method by 5.6％and 5.8％,respectively,on real telemetry parameters.Compared with other anomaly de-tection algorithms,the proposed method has strong ability to discriminate normal and abnormal sam-ples,effectively solves the problems of lack of differentiation of features and insufficient extraction of de-cision information,and has good noise immunity and effectiveness.
Scientific satelliteTelemetry parametersAutoencoderAnomaly detectionLatent space optimization
万方数据
维普
