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

Very Low Earth Orbit (VLEO) Spacecraft Operations

Advanced Satellite Systems, Sensors and Intelligence

Very Low Earth Orbit (VLEO) is a region of space above the Kármán line and up to The International Space Station at approximately 450km altitude. This region is a challenge to operate in due to atmospheric effects limiting the total lifespan of a typical satellite.

This project will investigate the development of a purpose-built attitude and orbit control system (AOCS) for operation in VLEO to create new mission opportunities, improving data products and communication services. The operational advantage of closer proximity to ground and high revisit rates will bring enhanced commercial products and capabilities to consumers, expanding the ease and access to space.

The new AOCS will bring innovation on orbital control in regions of significant atmospheric density, coupling new propulsion methods and attitude manoeuvring concepts for sustainment of new small satellite operations.


Project Leader:
Patrick Wang, SpaceOps Australia


Success Factor-based Framework for Undertaking Small Satellite Missions

Advanced Satellite Systems, Sensors and Intelligence

This project aims to conduct a case study on the process of development of the South Australian Government’s first satellite mission, Kanyini.

The proposed research will capture the issues, activities, challenges and opportunities for improvement the process of conception, design, manufacture, testing, launch and operation of small satellite missions and identify learnings that will guide future missions and process improvements. The proposal given here covers the space mission’s development up until completion of ground qualification testing.

A second follow-on project will follow the mission through its launch and on-orbit operations.


Project Leader:
Dr Mahmoud Efatmaneshnik, University of South Australia


Space Analytics Engine for On-Board Machine Learning and Multimodal Data Fusion

Advanced Satellite Systems, Sensors and Intelligence

Current ISR satellites typically play a passive data collection and dissemination role, with processing typically done further downstream at ground stations. This “offline” processing approach introduces significant delays in converting data to actionable insights, prevents low-latency coordination between end-users (e.g., field operatives) and space-based assets, and precludes more intelligent sensing capabilities; for example, adaptive tasking of the sensor suite based on real-time data enhancement and analytics to improve intelligence gathering.

The project builds upon existing work at BAE Systems and the partner academic institutions (UoA and UNSW) on machine learning for ISR applications. The project will develop novel algorithms and workflows to enable machine learning on nanosatellites for space based ISR from multi-modal sensors. Note that the capacity of current edge computing hardware (e.g., Nvidia Jetson series) is still significantly smaller than standard hardware, thus necessitating algorithms for model pruning and data pre-processing to perform on-board machine learning. Operating in space also presents unique obstacles to updating the pre-trained on-board models, in terms of procuring data and supervisory labels for retraining, and bandwidth constraints in updating models. The project will tackle the above challenges to produce a novel space analytics engine that is reconfigurable after launch, which significantly increases the value proposition of on-board processing.


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


Small Satellite Thermal Management with 3D Printed Metal Heat Sinks Containing Phase Change Material Thermal Storage

Advanced Satellite Systems, Sensors and Intelligence

The product that I aim to produce, as a result of my research, is an enabling technology for next generation high-powered small satellites that has many applications and is very much aligned to SmartSat’s priority research areas in Advanced Communication, Advanced Satellite Systems and Next Generation Earth Observation Data Services.

The technology that I aim to develop will provide thermal management of high-powered small satellite electronics and increase their usability, improving satellite services and connectivity. Given the enabling application of my research to all small satellites, it can be applied to multiple sectors which includes Agriculture, Mining and Resources, Disaster and Emergency Management, Water and Environmental Management.

In concert with the SmartSat’s overarching strategy, the technology developed by my research is aimed to build capacity within the space sector via the: culmination of knowledge translation and creation resulting in a novel way to prevent high powered small satellites from overheating, with the ultimate aim to commercialise a product that will provide capability to the Australian space industry and manufacturing in South Australia.


Project Leader:
Professor Peter Murphy, University of South Australia

PhD Student:
Artur Medon


Satellite-based GNSS-Reflectometry for Maritime Surveillance, Observation and Detection Phase 1

Advanced Satellite Systems, Sensors and Intelligence

The aim of this project is to establish the theoretical feasibility of Global Navigation Satellite System (GNSS) reflectometry in detecting objects of interest at sea, and defining performance characteristics associated with this technology.


Project Leader:
Professor Andrew Dempster, University of New South Wales


Responsible AI in Space

Advanced Satellite Systems, Sensors and Intelligence

In the 2010s, Global Space Governance (GSG) became an urgent issue with the growing commercialisation of outer space. Given the technical and operational complexities surrounding such enterprise, rather than adopting the traditional model of treaty making, new thinking was required to address the challenges and opportunities created by this commercialisation. In 2014, the Montreal Declaration on Global Space Governance created a Working Group to make recommendations on the peaceful and sustainable use of outer space. In 2017, the recommendations were published by the Institute and Centre of Air and Space Law at McGill University. This international study identified safety and technical gaps in the existing governance regime.

While the McGill study identifies gaps in existing space governance, it does not provide specific recommendations on how different types of technology should be regulated by the space sector. One of these technologies is Artificial Intelligence (AI). The use of AI in the space sector is both a challenge and an opportunity. Challenges include protecting the rights of all stakeholders in the harvesting of data sourced from outer space operations. Opportunities include the ability to provide control systems that enhance traffic safety in outer space.

There is therefore a need to extend existing GSGs to AI applications.

This project aims to create a field-validated AI governance framework for the Australian space sector.


Project Leader:
Professor Mirko Bagaric

PhD Student:
Thomas Graham