Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness - Ralph, Nicholas; Joubert, Damien; Jolley, Andrew; Afshar, Saeed; Tothill, Nicholas; van Schaik, André; Cohen, Gregory (Frontiers in Neuroscience, 2022)
Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems that support the international community’s national, commercial and defence interests. This resource is rapidly becoming depleted with over-crowding in high demand orbital slots and a growing presence of space debris. We propose the Fast Iterative Extraction of Salient targets for Tracking Asynchronously (FIESTA) algorithm as a robust, real-time and reactive approach to optical Space Situational Awareness (SSA) using Event-Based Cameras (EBCs) to detect, localise and track Resident Space Objects (RSOs) accurately and timely. We address the challenges of the asynchronous nature and high temporal resolution output of the EBC accurately, unsupervised and with few tune-able parameters using concepts established in the neuromorphic and conventional tracking literature. We show this algorithm is capable of highly accurate in-frame RSO velocity estimation and average sub-pixel localisation in a simulated test environment to distinguish the capabilities of the EBC and optical setup from the proposed tracking system. This work is a fundamental step toward accurate end-to-end real-time event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardised tracking metrics.