An LSTM-based Maneuver Detection Algorithm from Satellites Pattern of Life

Abstract

Near Earth Environment is swiftly turning into an overpopulated operational space, mainly due to increased commercial missions and service-aimed constellations. As a consequence, the development of an efficient Space Traffic management infrastructure is progressively becoming a mandatory requirement. In this framework, Space Surveillance and Tracking programs play a key role by taking care of the entire measurement processing pipeline and maintaining Resident Space Object catalogs by updating orbital data for each tracked target. Collecting a vast quantity of structured data represents the perfect use-case for data-driven techniques to mine for hidden patterns and features within them. This work shows how a Long-Short-Term-Memory Neural Network, specialized in time sequences analysis, can take advantage of an operational object's Pattern of Life, consisting of its state and maneuvering history, and perform maneuver detection on new incoming orbital parameter sequences. These data prove fundamental in progressively labeling a target orbit evolution, characterizing its operational life, and detecting mission phase changes. They also help in providing a deeper context to an operator performing any of the following tracking-related activity, adding background information retrieved from the effective processing of a target's history.