Satellite Telemetry Data Anomaly Detection Using Causal Network and Feature-Attention-Based LSTM

Most of the data-driven satellite telemetry data anomaly detection methods suffer from high false positive rate (FPR) and poor interpretability. To solve the above problems, we propose an anomaly detection framework using causal network and feature-attention-based long short-term memory (CN-FA-LSTM). In our method, a causal network of telemetry parameters is constructed by calculating normalized modified conditional transfer entropy (NMCTE) and optimized by conditional independence tests based on the conditional mutual information (CMI). Then, a CN-FA-LSTM is established to predict telemetry data, and a nonparametric dynamic
$k$
-sigma threshold updating method is proposed to set thresholds. A case study on a real satellite demonstrates that anomaly detection using the CN-FA-LSTM and nonparametric dynamic
$k$
-sigma threshold updating has an average F1-score of 0.9462 and an FPR of 0.0021, which are better than the baseline methods. Furthermore, CN-FA-LSTM has a stronger interpretability than other commonly used prediction models. Supplementary experiment on two public datasets verifies the universal applicability of our method.