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In this project the authors look at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate: If event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

With the “democratisation of space”, and the proliferation of large non-geostationary orbit (NGO) constellations, the pace at which human-made objects are being deployed in orbit demands urgent action in defining and supporting a global Space Traffic Management (STM) plan. Promptly identifying, tracking, and cataloguing of resident space objects (RSOs), in particular after orbit insertion or break-up and fragmentation events, as the recent Russian anti-satellite weapon (ASAT) test put in evidence, is of critical importance to enable a safe and sustainable use of space.

The main objective of this study is the evaluation of the merits of a dual-camera setup for the acquisition of both high spatial resolution images and high-resolution images of identified events of
interest, on-board, through AI methods. A mission analysis of the approach for a single-satellite, dual camera setup as well as for a two-satellite (leading-trailing), single camera setup was

Persistent Responsive Real-Time Earth Observation small satellite constellation mission (PERTEO) is a study dedicated to provide a global democratic service to support disaster management. Earth observation and satellite-based communications play a key role to support disaster management targeting responsiveness and the persistence of the service. In the terminology of on-demand Copernicus services, responsiveness is determined by the lead-time and latency. The persistence is determined by the availability and by the revisit time of the service.