SmartSat is integrating the capabilities from the priority areas set in SmartSat’s Technology Roadmap across three primary research program areas as below.

The ever-increasing number of satellites being launched into space will pose significant challenges in tracing satellites, avoiding collisions in an increasingly crowded space and integrating different technologies and systems.  As satellite technology becomes physically smaller and are deployed in constellations, increased opportunities exist for significant processing and Artificial Intelligence (AI) techniques to be out on-board satellites so that some advanced analytics are carried out on-board satellites to enhance the efficiency and effectiveness of data gathering and analysis.

See projects in Advanced Satellite Systems, Sensors and Intelligence projects below:

Topics include:

  • MBSE & Digital twins of small satellite systems
  • Autonomous, cooperative satellite formations
  • Artificial Immune Systems in satellite swarms
  • Trusted Autonomous Formations
  • Self-healing satellite systems
  • Agile & resilient satellites
  • Satellite system & data security
  • Advanced pointing & maneuvering
  • On-board machine learning modules
  • Advanced adaptable payloads
  • HgCdTelR Optoelectronic sensors
  • Quantum sensors

Pulsed Cathodic Arc Thruster Cathode Spot Motion and Erosion Pattern Quantification

Advanced Satellite Systems, Sensors and Intelligence

Neumann Space is developing an electric propulsion system that uses plasma from a solid conductive cathode as the propellant source.

Efficient propellant utilisation requires efficient cathode erosion, where the cathode spots erode the cathode working face to produce an azimuthally smooth pattern, while keeping the working face as close to perpendicular to the thrust vector as possible.

This project shall determine the cathode spot motion, and associated erosion pattern, of the arc discharge across a range of parameters, which shall help to inform future design choices.


Project Leader:
Dr Patrick Neumann, Neumann Space


Hybrid Space-Based Cameras for Target Uncertainty

Advanced Satellite Systems, Sensors and Intelligence

To allow high-resolution image capture of fast-moving objects from an orbiting camera, this project seeks to integrate a wide-field-of-view (wide-fov) conventional optical imaging sensor with a telescopic camera. Data from the wide-fov conventional optical imaging camera can be used to trigger the telescopic camera to get high resolution images of the target objects, even when there is some uncertainty around where exactly the fast-moving object will be at a given time. The process also helps reduce the bandwidth requirements for space-based sensors.

Fast-moving objects of interest include hypersonic systems, including weapons systems, as well as orbiting systems. This has applications for defence, SDA and in-orbit inspection domains.

The project will seek to develop a prototype hardware system as well as a preliminary concept-of-operations to enable the camera to perform as required.


Project Leader:
Dr William Crowe, HEO Robotics


Distributed FlatSats Phase 1: Use Case Scoping and Infrastructure Feasibility Study

Advanced Satellite Systems, Sensors and Intelligence

This project shall investigate the user needs, technical options for, and technical feasibility of infrastructure for hosting Distributed FlatSats (DFS).

The DFS Infrastructure, to be prototyped in a follow-on project, would be a distributed simulation testbed for satellite equipment and software making use of internet connections between various items of user hardware and software. The DFS infrastructure design would be optimised for space start-ups and developers of small spacecraft in general, and it would be capable of supporting developmental testing of multiple different satellite mission

This is the first of a series of collaborative Aurora Programs we plan to run, supported by funding from SmartSat CRC and its partners.

Aiming to de-risk satellite-payload bus integration and test, the Aurora Space Cluster called for Expressions of Interest in 2021 to develop requirements for the new Aurora Distributed FlatSat, to be hosted at SmartSat CRC member ACSER @ UNSW Sydney.

ACSER @ UNSW Sydney produced a Distributed FlatSat Survey Infographic that briefly summarises the EOI’s as well as useful technical insights of end-users.

Distributed FlatSat Survey Infographic



Project Leader:
Joon Wayn Cheong, University of New South Wales


Development of an Evil Digital Twin for LEO Small Satellite Constellations

Advanced Satellite Systems, Sensors and Intelligence

Develop a proof of concept Evil Digital Twin framework for satellite related cyber security testing projects, with supporting use cases, suited for deployment on an experimental test bed.


Project Leader:
Professor Jill Slay, University of South Australia


Cybersecurity of Space Infrastructure: A Multidisciplinary Approach

Advanced Satellite Systems, Sensors and Intelligence

This project aims to identify concrete cyber threats that currently exist within the Australian space market, clarify the policy and legal protection available to satellite operators in case of cyber incidents, and recommend a set of security controls falling within both the technical and policy dimensions.

It will do so by enhancing and exploiting a space cyber reference architecture developed by CyberOps through the definition of 10 initial use cases of space cyber threats that exist in satellite missions. These threat vectors will be then used to recommend a suite of security controls that can guide future R&D activities by space operators.

An investigation into the policy landscape that surrounds these vectors will be also carried out to inform specialists about the policy and legal frameworks they operate in when developing the controls to mitigate the threat vectors.


Project Leader:
Associate Professor Rodrigo Praino, Flinders University


Building Damage Estimation After Natural Disaster Using Multi Satellite Source Data based on Machine Learning

Advanced Satellite Systems, Sensors and Intelligence

Building damage assessment after natural disasters is an important task for disaster management. In order to provide detailed levels of post-event building damage, this research will develop a deep learning building classification model with an online database using transfer learning on both 2D and 3D data.

The damage level is categorized into no damage, minor damage, major damage and collapsed. Natural disasters, such as fires, earthquakes and tsunamis, can cause serious building damage. Hence, the theoretical contribution of this study is providing a developed method for building damage classification. The practical contribution is supporting the government and rescue teams in their disaster response and decision making. It can also provide quick information for disaster management.


Project Leader:
Professor Linlin Ge, University of New South Wales

PhD Student:
Chang Liu, University of New South Wales