Onboard Machine Learning for Intelligent Satellites

This project aims to develop a dynamic system of analytical techniques operating on multiple modalities of data in Earth Observation. It will use insitu sensing to conduct on-board analytics, hence reducing the required bandwidth costs of communication. Reducing the need for human agents to interpret and respond to gathered data will give satellites advanced autonomy, accelerating the gathering and transmission of higher-fidelity data. While this project will target ISR, the resulting system can be configured to any domain, and can be quickly customised for industry use. Areas that can benefit include Disaster and Emergency Management, where tracking and responding to an evolving situation quickly means crucial information is communicated efficiently. Water and Environmental Management too, where real- me hydrological analysis is needed to gauge Mow changes, or real-time monitoring of forests and Agricultural crops can detect climate events or pest infestations. This project will also investigate the e1ects malicious actors can potentially achieve against the system. Exploring these and their associated defences will contribute to satellite resilience. Utilising multiple modalities of data as a defence and efficiency technique will also demonstrate a novel use of data fusion.


Project Leader:
Professor Tat-Jun Chin, The University of Adelaide

PhD Student:
William Meakin