Deep-Learning-Based Tracing for Satellite Telemetry

Abstract

The mechanical section of the telemetry data from the scientific satellite “Lomonosov”, (such as yaw, pitch, and roll angles of the spacecraft’s main axes, along with its programmed and measured velocities) is pre-processed (alignment, reflection, and binarization) and used for anomaly behavior propagation. The main goal of this study is to estimate possible abnormal behavior of the system in the future and to help to restore normal behavior during a limited communication session with a spacecraft. The system model uses a recurrent architecture approach, namely tracing methodology, considering time shifts in the target data sequence. A deep learning strategy is used to model the abnormal behavior using the onboard collected mechanical information as inputs. The results are compared with the onboard anomaly detection system (ARO) data. The reproduction of the obtained information shows better performance compared to traditional estimation techniques, using binary cross-entropy and receiver operating characteristic curve (ROCAUC) as comparison criterion. Future model modifications, which can improve its quality, are discussed at end of the study.