Abstract: Satellite telemetry data is crucial for reflecting the operational status of satellites during their in-orbit operation, and anomaly detection in such data is of significant importance for ensuring the safety and stability of satellites. In engineering practice, the traditional detection method of manual preset threshold comparison is widely used, but the method is difficult to effectively recognize the anomaly patterns within the threshold range. At the same time, some complex anomaly detection algorithms still exhibit limitations in performance. To address these issues, a novel anomaly detection method based on joint analysis in time-frequency domain is proposed. By performing time-frequency decomposition, feature extraction, and reconstruction error calculation on the telemetry data, this method enables accurate identification of abnormal data. Experimental results demonstrate that this approach can efficiently capture anomalous patterns across several publicly available anomaly detection datasets and exhibit strong generalization capability, providing a reliable anomaly detection solution for satellite operations.

Key words: anomaly detection, telemetry data, time-Frequency variation, reconstruction error