Neuromorphic Sensing and Processing for Space Domain Awareness

As space debris poses substantial risks to space-based assets, the need for efficient, high-resolution monitoring and prediction methods is pressing. This paper presents the findings from the project NEU4SST, exploring Neuromorphic Engineering, specifically event-based visual sensing coupled with Spiking Neural Networks (SNNs), as a solution for enhanced Space Domain Awareness (SDA). Our research concentrates on event-based visual sensors and SNNs, offering low power consumption and precise high-resolution data capture and processing. These technologies bolster the ability to detect and track objects in space, addressing key challenges in the Space domain. Our method exceeded previous models by 15% on the informedness metric, demonstrating its potential in improving SDA, and aiding safer, more efficient space operations. Continued research and development in this area are crucial for realising the full potential of Neuromorphic engineering for future space missions.