The increasing complexity of space operations, driven by the proliferation of satellites and growing risks of orbital congestion, necessitates advancements in Distributed Satellite Systems (DSS) and Space Traffic Management (STM). This thesis presents a comprehensive framework that integrates next generation autonomy methods using distributed coordination and supervisory control to ensure mission assurance, resilience, and adaptability in DSS Space-Based Space Surveillance (SBSS) missions in support of STM.

Key Contributions
Holistic Framework for Intelligent DSS in STM:
This research develops a unified framework for DSS operations, emphasizing distributed mission planning, task coordination, and real-time adaptability. It highlights the critical architectural elements and autonomy features required to optimize satellite collaboration in surveillance and STM tasks.

Distributed Mission Planning and Resource Optimization:
The thesis introduces a distributed, multi-agent mission planning strategy leveraging advanced optimization techniques to coordinate observation tasks effectively across satellite networks. These strategies, maximize global utility, and adapt dynamically to changing operational requirements.

Real-Time Adaptive Trajectory and Attitude Optimization:
A robust onboard optimization framework is designed to manage satellite manoeuvres and resource constraints. The algorithms enable satellites to autonomously adapt to evolving mission scenarios, ensuring timely and efficient execution of SBSS operations and collision avoidance.

Supervisory Control and Mission Assurance:
The research proposes an integrated approach of providing supervisory control mechanisms based on dynamic system reliability assessment. This ensures robust oversight of autonomous DSS operations through intelligent plan selection enhancing mission resilience under uncertainty and operational disruptions.

Uncertainty Management and Probabilistic Modelling:
The thesis addresses the challenges of uncertainty in RSO tracking and collision prediction by developing unified methods for quantifying and resident space object uncertainties to support collision avoidance activities.

Impact and Applications:
The proposed frameworks and methodologies significantly enhance DSS capabilities in autonomous decision-making, distributed task coordination, and uncertainty management. These contributions support critical STM goals, including collision avoidance, resource optimization, and mission resilience. The research has broad applications in managing the increasing demands of DSS operations, ensuring safe and sustainable use of space for future generations.