Intelligent Distributed Satellite System for Space Based Space Surveillance

Abstract

Resident Space Objects (RSO) are human-made objects in orbit around Earth and can remain there for an extended period. These objects can include active satellites, rockets, and space stations, as well as debris caused by previous space endeavours. Debris originated as a consequential outcome of activities such as space launches, orbital missions and collision events, pose a formidable threat to currently operational space assets. To reduce the risk of on-orbit collisions, it is essential that spacecraft operators enhance their situational awareness concerning potential threats posed by RSO. This necessitates comprehensive tracking of the RSO in space, continuous orbit determination and estimation of the probability of accidental collisions. Effective Collision Avoidance (CA) manoeuvres rely on accurate tracking and characterization of RSO. Currently, RSO are monitored and catalogued using ground-based observational systems. However, Space-Based Space Surveillance (SBSS) presents a viable solution for tracking the RSO, providing superior sensor resolution, tracking accuracy, and independence from weather conditions. Accurate and continuous orbit determination of RSO is critical for developing a robust framework that enables accurate estimation of RSO dynamics. Given the evolving complexity of the space domain a paradigm shift towards the development of autonomous, resilient, and intelligent space surveillance architectures capable of operating with minimal ground segment intervention is necessary. Considering this, the Intelligent Distributed Satellite Systems (IDSS) is a promising concept for the future of Space Situational Awareness (SSA) and Space Traffic Management (STM) as these architectures move away from the monolith system concept to adopt multiple elements that interact, cooperate, and communicate with each other resulting in new systematic properties and/or emerging functions.
This thesis aims to develop novel methodologies and algorithms for the design and operational management of advanced space assets, such as IDSS, for SBSS tasks. The proposed approaches account for complex and nonlinear influences including astrodynamic perturbations, navigation and tracking performance, and factors impacting atmospheric flight phases with the broader objective of contributing to Space Domain Awareness (SDA) in support of future STM operations. As part of this effort, the thesis proposes a unified approach that integrates RSO tracking with autonomous navigation and maneuvering algorithms as an integral part of the overall system design. This thesis also proposes a multi-sensor data fusion strategy designed to integrate angular measurements derived from image sequences captured by multiple Electro-Optical Sensors (EOS) deployed in SBSS missions. A key contribution lies in the development of data fusion frameworks optimized for constrained computational environments, thereby enabling seamless real-time implementation within IDSS platforms. Verification case studies are performed to evaluate the effectiveness of the proposed methodologies under varying operational scenarios. Results corroborate the effectiveness of the proposed algorithms highlighting its potential for future SDA applications.

Date of Award	2025
Original language	American English
Supervisor	Roberto Sabatini (Supervisor)