Orbital Space Situation Awareness using Neuromorphic Systems

Research Themes

Sensor Signal Processing

Autonomous Sensing Platforms

Aim

Research on the algorithm development for automatic target detection, tracking and characterisation using event camera and spike neural networks.

Objectives

1. Space Situation Awareness with Neuromorphic Systems
2. Onboard processing with SNN
3. Going beyond detection based on object shapes
4. Object characterisation using micro-vibration

Description

Space Situational Awareness (SSA) has become a necessity in this congested space era. The fact is that finding moving objects, which may or may not be threats, in space is like finding a needle in a haystack. Neuromorphic imagers capture only changes in scene luminance and are quiescent when there is no change. This massively reduces data generation, but it requires a different processing chain to perform the task. Neuromorphic processing is power-efficient for embedded implementations of event data.

This project investigates neuromorphic processing for SSA applications using event-camera data. The aim is to use real data to characterise how objects of interest can be detected and characterised using shape, motion-track, and micro-vibration features. This can provide a step change over current capabilities, in which most processing occurs on the ground, and help avoid the bandwidth bottleneck of transmitting full imagery to Earth, and real-time detection may be possible.

A UK first class, or equivalent international degree on Electrical Engineering, Physics, Computer Science, Mathematics or similar. 

Further information on English language requirements for EU/Overseas applicants.

Full funding available for this position