Satellite On-Orbit Anomaly Detection Method Based on a Dynamic Threshold and Causality Pruning

IEEE Access

Xinyu Ma¹  Jin G.²  Siya Chen² 

[1] Beijing Aerospace Control Center, Beijing, China;College of Systems Engineering, National University of Defense Technology, Changsha, China;

关键词: Dynamic threshold;    causality pruning;    anomaly detection;    GRU;    on-orbit satellite;   

DOI  :  10.1109/ACCESS.2021.3088439

来源: DOAJ

【 摘 要 】

It is difficult for existing deep learning-based satellite on-orbit anomaly detection methods to define the residual-based detection threshold and identify false anomalies. To solve the above problems, this paper proposes both a detection threshold determination and dynamic correction method and a causality-based false anomaly identification and pruning method. We use the GRU (Gated Recurrent Unit) to model and predict the telemetry parameters to obtain the residual vector; determine and dynamically correct the threshold according to the prescribed false positive rate; propose an improved multivariate transfer entropy method to identify the causal relationships between the telemetry parameters; and, based on the causality, determine whether the detected parameter anomalies are false. Experiments show that the precision, recall, and F1-score of the method proposed in this paper are superior to the current typical method, and the false positive rate is significantly reduced, demonstrating the effectiveness of the proposed method.

【 授权许可】

Unknown