卫星遥测数据是卫星在轨运行过程中产生的重要数据,全面反映了卫星的运行状态,检测其中异常对维护卫星的安全与稳定具有重要意义。在工程实践中,传统的人工预设阈值比对的检测方法应用广泛,但该方法对阈值范围内的异常模式难以有效识别。同时,一些复杂的异常检测算法在性能表现上仍存在局限性。为了解决上述问题,提出了一种基于时频域联合分析的异常检测方法,通过对遥测数据进行时频分解、特征提取与重构误差计算,实现了对异常数据的精准识别。实验结果表明,在多个公开的异常检测数据集上,该方法能高效捕获多种异常模式,并展现出较强的泛化能力,为卫星运行提供了一种可靠的异常检测手段。
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.
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