Abstract: To ensure the safe and stable operation of satellites, it is of paramount importance to promptly conduct data mining, situation analysis, and abnormal response for telemetry parameters. Given the limitations of existing methods in effectively addressing satellite telemetry parameter anomalies, this paper introduces an innovative anomaly detection method that leverages temporal interpolation and Generative Adversarial Networks (GANs). The proposed method employs a one-dimensional Convolutional Neural Network (1DCNN) to extract temporal features from the telemetry data. These features are then used to model the distribution of telemetry parameters through a generative adversarial network, which consists of a generator and a discriminator that are trained simultaneously to learn the distribution of normal data. The method innovatively incorporates a detection approach based on interpolation, which significantly enhances the accuracy of anomaly detection and the capability to handle complex and subtle anomalies that may not be easily identified by traditional methods. The effectiveness of the proposed method is validated through comprehensive testing on real satellite data as well as established public datasets. Results demonstrate that, when compared with various existing anomaly detection methods, the proposed approach achieves the highest F₁ scores on the majority of the datasets tested. This indicates a superior balance between precision and recall, which is crucial for reliable anomaly detection. Furthermore, the method exhibits good stability under different anomaly densities, suggesting its robustness in varying operational conditions. This research outcome not only enhances the understanding of satellite telemetry anomalies but also provides strong decision support for ground operation in satellite mission analysis and anomaly handling, thereby contributing to the overall safety and efficiency of satellite operations.