Alejandro Hernández Díaz

The planning and execution of modern space missions rely on traditional SSA methods for detecting and tracking orbiting hazards. This often leads to sub-optimal responses due to remote sensing inaccuracies and transmission delays. On the other hand, deploying and maintaining space-based sensors is expensive and technically challenging due to the inadequacy of current vision technologies. In this paper, we propose a novel perception framework to enhance in-orbit autonomy and address the shortcomings of traditional SSA methods. We leverage the advances of neuromorphic cameras for a vastly superior sensing performance under space conditions. Additionally , we maximize the advantageous characteristics of the sensor by harnessing the modelling power and efficient design of selective State Space Models. Specifically, we introduce two novel event-based backbones, E-Mamba and E-Vim, for real-time on-board inference with linear scaling in complexity w.r.t. input length. Extensive evaluation across multiple neuromorphic datasets demonstrate the superior parameter efficiency or our approaches (

Advancements in onboard data processing capabilities of small EO satellites represent an avenue for mission integrators, satellite customers and end-users alike to maximise the return on investment of space-borne remote sensing platforms. Surrey Satellite Technology Limited's (SSTL's) Flexible and Intelligent Payload Chain (FIPC) subsystem is an integrated solution which aims to address the data bottleneck challenges of small EO satellites, leveraging capabilities which include onboard data processing. This publication describes SSTL's recent coupled developments in the FIPC space segment, towards a tightly integrated hardware architecture; a new Linux-based custom onboard processing environment; and an end-user segment with a tailored Application Development Framework. Together these facilitate the deployment of in-house and third-party developed software onboard processing Applications and pipelines, including those which exploit machine learning (ML) libraries and frameworks.