A Review of Spatiotemporal GANs for Telemetry Data Anomaly Detection





Telemetry Data, Anomaly Detection, Spatial-Temporal Generative Adversarial Networks, ST-GANs, Performance Evaluation


Telemetry data anomaly detection is a crucial task in various domains, including aerospace, power systems, and environmental monitoring. In recent years, significant advancements have been made in the development of anomaly detection techniques, particularly with the advent of spatial-temporal generative adversarial networks (ST-GANs). This review paper aims to provide a comprehensive overview of the progress in telemetry data anomaly detection, with a specific focus on the application of ST-GANs. The review begins by emphasizing the importance of telemetry data anomaly detection and highlighting the challenges associated with traditional methods. Subsequently, it delves into the underlying principles of ST-GANs and their suitability for detecting anomalies in complex, time-series data. The paper presents a detailed analysis of experimental results and performance comparisons of ST-GANs with other state-of-the-art anomaly detection algorithms, such as LSTM-GAN, Isolation Forest, and GRU-VAE.


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Author Biography

Sushant Singh, Amity Institute of Space Science and Technology, Amity University Uttar Pradesh, India-201303

Sushant Singh is a dedicated researcher and scholar in the field of space science. Currently affiliated with the Amity Institute of Space Science and Technology at Amity University, Noida, Uttar Pradesh, India. Sushant's academic journey began with a Bachelor's degree in Aerospace Engineering, where he developed a strong foundation in the principles of aerodynamics, propulsion systems, and spacecraft design. Driven by a passion for cutting-edge technologies and a desire to push the boundaries of aerospace engineering, he pursued a Master's degree in Avionics, delving into the intricacies of electronic systems, control systems, and data acquisition mechanisms in aerospace vehicles.

With a solid background in both theoretical and practical aspects of aerospace engineering, Sushant embarked on a research endeavor focused on the application of spatial-temporal generative adversarial networks (ST-GANs) for anomaly detection in telemetry data. His ground-breaking work in this field has been instrumental in addressing the challenges associated with monitoring and maintaining the integrity of critical systems in aerospace vehicles, spacecraft, and unmanned aerial vehicles (UAVs). Sushant's comprehensive review paper, "A Review of Spatiotemporal GANs for Telemetry Data Anomaly Detection," stands as a testament to his expertise and dedication. Through meticulous analysis and synthesis of the latest research, he has provided a comprehensive overview of the theoretical foundations, methodological considerations, and practical implications of leveraging ST-GANs for anomaly detection across various industrial domains.




How to Cite

Singh, S. (2024). A Review of Spatiotemporal GANs for Telemetry Data Anomaly Detection. Acceleron Aerospace Journal, 2(3), 195–203. https://doi.org/10.61359/11.2106-2411