Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97.

An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. SMI distance measurement using compressed sensing (CS) is proposed in this work to relieve this burden. The SMI signal was sampled via a measurement matrix at a sampling rate lower than Nyquist’s law and then recovered by the greedy pursuit algorithm. The recovery algorithm was improved to increase its robustness and iteration speed. On a distance measuring system with a measurement error of 60 µm, the difference between raw data with 1800 points and CS recovered data with 300 points was within 0.15 µm, demonstrating the feasibility of SMI distance measurement using CS.

The rotational mechanism, which plays a critical role in energy supply, payload antenna pointing, and attitude stabilization in satellites is essential for the overall functionality and performance stability of the satellite. This paper takes the space turntable of a specific satellite model as an example, utilizing high-frequency and high-dimensional telemetry data. An improved informer model is used to predict and diagnose features related to the turntable’s operational health, including temperature, rotational speed, and current. In this paper, we present a forecasting method for turntable temperature data using a hybrid model that combines singular spectrum analysis with an enhanced informer model (SSA-Informer), comparing the results with threshold limits to determine if faults occur in the satellite’s rotational mechanism. First, during telemetry data processing, singular spectrum analysis (SSA) is proposed to retain the long-term and oscillatory trends in the original data while filtering out noise from interference. Next, the improved informer model predicts the turntable temperature based on the mapping relationship between the turntable subsystem’s motor current and temperature, with multiple experiments conducted to obtain optimal parameters. Finally, temperature thresholds generated from the prediction results are used to forecast faults in the rotational mechanism over different time periods. The proposed method is compared with current popular time-series prediction models. The experimental results show that the model achieves high prediction accuracy, with reductions of at least 10% in both the MAE and MSE than CNN-LSTM, DA-RNN, TCN-SE and informer, demonstrating the outstanding advantages of the SSA and improved informer-based method in predicting temperature faults in satellite rotational mechanisms.

The magnetometer is a vital measurement component for attitude measurement of near-Earth satellites and autonomous magnetic navigation, and monitoring health is significant. However, due to the compact structure of the microsatellites, the stray magnetic changes caused by the complex working conditions of each system will inevitably interfere with the magnetometer measurement. In addition, due to the limited capacity of the satellite–ground measurement channels and the telemetry errors caused by the harsh space environment, the magnetic data collected by the ground station are partially missing. Therefore, reconstructing the telemetry data on the ground has become one of the key technologies for establishing a high-precision magnetometer twin model. In this paper, firstly, the stray magnetic interference is eliminated by correcting the installation matrix for different working conditions. Then, the autocorrelation characteristics of the residuals are analyzed, and the TCN-SE (temporal convolutional network-squeeze and excitation) network with long-term memory is designed to model and extrapolate the historical residual data. In addition, $MAE$ (mean absolute error) is used to analyze the data without missing at the corresponding time in the forecast period and decreases to 74.63 nT. The above steps realize the accurate mapping from the simulation values to the actual values, thereby achieving the reconstruction of missing data and establishing a solid foundation for the judgment of the health state of the magnetometer.