Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness

Frontiers in Neuroscience

Andrew Jolley¹  Saeed Afshar²  André van Schaik²  Gregory Cohen²  Damien Joubert²  Nicholas Tothill²  Nicholas Ralph² 

[1] Air and Space Power Development Centre, Royal Australian Air Force, Canberra, ACT, Australia;International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia;

关键词: event-based;    tracking;    space situational awareness;    machine learning;    neuromorphic;    image processing;   

DOI  :  10.3389/fnins.2022.821157

来源: DOAJ

【 摘 要 】

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, localize, 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 localization 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 optical event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardized tracking metrics.

【 授权许可】

Unknown