Self-ensemble satellite anomaly detection method for center-constrained contrastive learning

To deal with the problem of the existing telemetry anomaly detection algorithms, such as the poor discrimination capability of the feature, and loss of anomaly decision-making information, a self-ensemble anomaly detection method based on center-constrained contrastive learning was proposed. The method mapped the normal samples to a compact feature distribution by combining contrastive loss and center loss, and a multi-view and multi-level ensembled feature decision method was used to obtain the anomaly detection of the sample. The method improves the adaptability of the model to the complex working conditions of the satellite. The real telemetry parameter data of scientific satellite and benchmark data set are used for verification. The proposed method is robust to noise, and achieves 21.8% improvement of F score than that of the state of the art method. The results of the experiment demonstrate the feasibility of the method, which can provide effective support for satellite operation.