Description
The objective is to develop an onboard data handling architecture capable of autonomously acquiring, processing and interpreting housekeeping and telemetry data and taking the required actions without ground intervention. The activity will develop a data handlingsub-system demonstrator to validate the intelligent functionalities, including prognostic and health management (PHM) capabilitiesTargeted Improvements: Enabling technology development for future fully autonomous satellites which are capable to operate independently, without the need of immediate operator intervention.Description: Current satellite operations are centred around human interactions from ground, which results in considerable inefficiencies. Limited windows of communication with the satellite, latency, and resource-limited ground infrastructure are the main reasons for the inefficiencies, which become more prominent for large constellations of satellites. This activity reduces the dependence of the spacecraft on ground stations by developing an intelligent onboard data handling architecture, using Artificial Intelligence (AI) to autonomously process, interpret housekeeping and telemetry dataand take decisions on needed actions in real-time. This is considered an essential capability for management of future satellite constellations. The current data handling architectures and core building blocks such as the onboard computer (OBC) and Remote Terminal Unit (RTU) need to be redesigned to allow autonomous telemetry acquisition for all spacecraft units (incl. telemetry format standardisation), data fusion of big data sets and processing with selected AI-based algorithms, derivation of performance profiles, identification of anomalous behaviour and failure prediction. Data acquisition, either directly by the OBC or by the RTU, will entail thedevelopment of a block capable to acquire, convert and calibrate all onboard telemetries at configurable sampling rates. Data fusion and processing will be facilitated by using AI and Machine Learning (ML), enabling the OBC to analyse large data sets and autonomously take the required actions. This includes detection of potentially hazardous situations, such as solar flares or collisions withspace debris, making intelligent decisions and taking appropriate actions to mitigate or avoid these situations. In addition to the development of the intelligent data handling architecture, the activity shall also provide the onboard computer (OBC) with the capability to execute an embedded version of a system digital twin. This digital twin implementation will enable the OBC to simulate and mirror the behaviour of the actual satellite system in real-time, allowing for enhanced monitoring, diagnostics, and performance optimisation. By incorporating a system digital twin into the OBC, it will be possible to achieve a higher level of autonomy and proactive decision-making within the satellite operations. This activity will develop a data handling sub-system demonstrator which willinclude breadboards of the OBC and RTU with AI/ML-based algorithms, to validate the developed intelligent functionalities, including Prognostic and Health Management (PHM) capabilities.