Autonomous Optical Sensing for Space-Based Space Surveillance

Space debris population has increased dramatically in the past decades posing a threat to the future of space operations. Traditionally, Resident Space Objects (RSO) are tracked and catalogued using ground-based observations. However, Space Based Space Surveillance (SBSS) is a promising technology to complement the ground-based observations as it offers greater performance in terms of detectability, accuracy and weather independency. A Distributed Satellite System (DSS) architecture is proposed for a SBSS mission equipped with dual-use star trackers and inter-satellite communication links to interact and cooperate with each other to accomplish optimized RSO tracking tasks while assumed to simultaneously perform earth observation tasks. This paper focuses on stereovision-based tracking algorithms with higher detectability and tracking accuracy in SBSS tasks in order to identify an optimal tracking solution for Space Domain Awareness (SDA), which could support future Space Traffic Management (STM) operations. Navigation and tracking uncertainties are analyzed in representative conditions to support the optimal selection and processing of individual observations and to determine the actual confidence region around the detected objects. Additionally, Particle Swarm Optimization (PSO) is implemented on-board the satellites to grant the DSS autonomous trajectory planning and Collision Avoidance (CA) manoeuvring capabilities.