Supervised learning algorithms for spacecraft attitude determination and control system health monitoring

The main concern of any space mission operation is to ensure the health and safety of spacecrafts. The worst case in this circumstance is probably the loss of a mission but the more common interruption of spacecraft functionality can result in compromised mission objectives. Spacecraft telemetry holds information related to state-of-health (SOH) of its subsystems. Each parameter has information that represents a time-variant property (i.e., a status or a measurement) to be checked. Moreover, the tremendous increase in telemetry data volume and its complexity directs the need for more efficient and scalable data processing systems. As a consequence, there is a continuous improvement of telemetry monitoring applications in order to reduce the time required to respond to changes in a spacecraft's SOH. So, a fast conception of the current state of the spacecraft is thus very important in order to respond to failures. Furthermore, the progressive growth in spacecrafts leads to increase of the level of standardization in spacecraft operations.