Trusted autonomous operations of distributed satellite systems for earth observation missions

For several decades, the design of satellites for outer space operations has predominantly followed a monolithic approach. These systems are characterised by high levels of integration and are developed with the primary goal of fulfilling a specified set of objectives that align with the requirements of the intended users. These systems have distinct spatial, control, and ground components that may remain unused or become deactivated subsequent to the completion of the operation. Historically, space missions have been regarded as specialised undertakings, prompting scientists to focus on the development of systems that maintain data and information segregation among satellites. The space industry is currently exploring the adoption of Distributed Satellite Systems (DSS) as a viable technological solution. This approach is particularly promising when combined with monolithic satellite systems, as research suggests that it can significantly enhance performance while simultaneously reducing costs. Recent developments in Artificial Intelligence (AI) technologies have demonstrated the crucial importance of autonomy within the contemporary landscape of space applications. The integration of AI approaches is necessary to achieve increased implementation and operation, hence ensuring the fulfilment of space mission objectives through autonomy. These strategies have demonstrated their capacity to execute, adjust, and react to alterations in the external environment autonomously, without the need for human intervention. Autonomy is considered a crucial characteristic for effectively managing contemporary distributed operations that necessitate collaboration and coordinated strategies. It enables the emergence of novel structural functions, such as opportunistic coalitions, resource sharing, and in-orbit data services. The implementation of Trusted Autonomous Satellite Operations (TASO) is necessary inside the framework of the DSS infrastructure in order to achieve this objective. The primary objective of this study is to explore and use of AI technologies in the context of TASO within DSS. This integration aims to enhance the capabilities of DSS by introducing intelligent DSS (iDSS). This research effort concentrates on the development of space and control segments, with a particular emphasis on enhancing the performance of iDSS. This is achieved through the utilisation of Cyber-Physical Systems (CPS) and the implementation of autonomous system designs. This thesis work investigated the potential of trusted autonomous operations in iDSS for Earth Observation (EO) Missions over Australia. Utilising Distributed Satellite Systems and Trusted Autonomy, the findings enhance Earth Observation operations such as disaster management and maritime monitoring. In this dissertation, a comprehensive approach is established for the design optimisation of an iDSS for EO. One of the objectives is to ensure persistent coverage over the Australian territory. These findings broaden the applicability of iDSS for outerspace operations.