Research Programs

Close Proximity Space Domain Awareness On-Board Satellite Systems

On-board high-performance computing

The research topic is in alignment with the CRC’s Advanced Satellite Systems, Sensors and Intelligence research program, and is highly relevant to the defence & security industry focus. The proposed research focuses on the development of intelligent satellite systems that have the ability to perceive and relay the semantics of any space situation through the processing of onboard sensor data. Thus, such research would follow the theme of building advanced satellite systems that leverage Artificial Intelligence (AI) techniques to perform advanced analytics on collected
sensor data. We currently call this capability “onboard Space Domain Awareness (SDA)”. Given that there is no universally recognised definition, SDA can be defined as the capability to detect, track, identify and characterise objects in space.

Through the partnership and collaboration with Infinity Avionics, the immediate intention is to look towards defence applications. In this context, the research has potential for application to tasks such as: 3D reconstruction, pose estimation and motion characterisation of space objects. Further, the proposed research aligns with many of the proposed future capabilities of existing platforms in the area of space logistics, such as the Mission Extension Vehicle (MEV) from Northrop Grumman. Future tasks in onboard autonomy such as satellite inspection, repair and in-orbit robotic assembly of space structures would immensely benefit from advances in close-proximity SDA.

Surveillance and tracking applications would also benefit from advancements made in SDA. Certain surveillance and tracking tasks, such as conjunction analysis (delivery of collision alerts between two objects) and fragmentation tracking (survey and characterise new debris emanating from a collision or explosion), rely on extracting semantics from a region of space to produce actionable intelligence — a function fulfilled by SDA. The purpose of the joint research and collaboration with Infinity Avionics is to develop cutting edge machine learning and computer vision algorithms for novel SDA-oriented space applications. Specifically, a key focus of the partnership is the development of algorithms that can leverage event cameras to achieve SLAM, pose estimation, etc. in a close-proximity space environment. These new capabilities are intended to extend the capabilities of onboard satellite perception systems by leveraging the unique operating characteristics of neuromorphic (event) cameras, namely their high dynamic range, high temporal resolution, low data rate and low power consumption properties.

Due to the unique constraints of operating within the space environment, sensors (and the algorithms that use them) need to operate in the face of challenging conditions: extreme lighting conditions, high velocity motion and low power consumption. Given the end-user’s current prowess in developing space-ready hardware (e.g. Orion12MP, SelfieCam), we hope to combine their expertise in developing space-ready hardware with our expertise in computer vision algorithms to develop a system that uses a monocular event camera to track the motion of a single (uncooperative) object in space. Towards this goal, we hope to use our combined expertise to develop algorithms that take the constraints of the operating environment into account and fully utilise onboard hardware to achieve efficient operation.

As technology in this area begins to mature, the proposed joint research will pivot towards attaining real-time operation of the object motion estimation algorithm and exploring potential multi-sensor architectures (e.g. event & LiDAR) to further improve the efficacy of the algorithm. To accommodate for these new challenges, new techniques must be devised that can fuse the additional information from the other sensors with the events.


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

PhD Student:
Ethan Elms, The University of Adelaide


Cyber Secure and Resilient Intelligent Space Systems

Trusted Autonomous Satellite Operations

UNSW proposes that the co-funded Chair part of our core partnership of SmartSat CRC be changed, to a project-based co-funded capability-building activity at UNSW Canberra in the form of funded positions within the university, whose combined efforts in the area of cyber resilient/secure artificial intelligence (AI) for space systems would grow critical mass and in turn an enduring centre of expertise for Australia in secure intelligent space systems R&D.

Accordingly, two excellent Level B and C academics will be recruited for three years, with the expectation that by the end of that period they will have successfully achieved promotion to Level C and D respectively, and (pending funding and faculty decisions leading up to that moment) will then be considered for conversion to a continuing appointment with ongoing salary provided by the university. Including on-costs and approximate salary increase due to band progression and enterprise bargaining increments, the cost of this will be approximately $1.05M over three years. The focus of will be on the development and TRL-raising of technologies for the deployment of cyber resilient/secure distributed intelligence satellite constellations.

The expected outcome is to produce state-of-the-art distributed-AI frameworks for cybersecure and resilient space systems. This will be in the shape of AI-algorithms, hardware testing, and demonstrations (simulations, emulations, and eventually deployment) of in-orbit AI-security space systems. This directly aligns with SmartSat’s Indo-Pacific Connector Capability Demonstration Program and will complement and add value to flagship technology developments such as the DSTG RMS Starshot and University of Adelaide/UNSW Trailblazer University Program space missions.


Project Leader:
Dr Bassel Al Homssi, University of New South Wales


Technology Development for In-orbit Servicing, Assembly and Manufacturing (ISAM)

Trusted Autonomous Satellite Operations

Maintaining satellites in orbit is challenging due to harsh space conditions and the potential risk of damaging expensive assets. This project will address the gaps between autonomous robotic systems and the requirements of real-time and reliable close proximity operations. We will focus on the on-board autonomy, advanced sensing, perception and robust control of robotic satellites. A ground-based ISAM mission demonstration with a double-armed robotic satellite will bring together developments in each project through an integrated demonstration of novel capabilities. This will advance technologies for extending the lifespan of Australia’s space assets and position Australian companies as leaders in the ISAM industry.


Project Leader:
Dr Xiaofeng Wu, The University of Sydney


An automated method of detecting, characterising, and responding to radiation events in space

Trusted Autonomous Satellite Operations

Resiliency is the ability of a system architecture to continue providing required capabilities in the face of system failures, environmental challenges, or adversary actions (Royal Australian Air Force, Space Command). As defined by the Resilient Multi-Mission Space STaR Shot, providing resilient space-based services direct to war fighters will enable the Australian Defence Force to prevail in increasingly contested environments.

The barrier to entry into the small satellite industry is lowering considerably in terms of manufacturing cost, time for construction, and cost to launch, enabling rapid experimentation and large constellations. Space has been listed as a Sovereign Industry Capability Priority (SICP) and there is a wide range of space applications that Australian Defence can undertake to achieve its goals in the harsh environment of space. With the shift in the space industry to small satellites using commercial-off-the-shelf products, this has reduced standards around space resiliency, and recent results have shown that approximately 40% of all small satellites launched in the last two decades experienced total or partial mission failure (Jacklin, 2018).

However, reduction in mission assurance has not reduced the operational mission expectation. In order to ensure a resilient spacecraft that meets the demand for Australian Defence capability, a spacecraft must be designed to survive in its environment and characterise and respond to threats in this changing environment. It is commonly known that space radiation has detrimental effects on electronic components in low-earth orbit. Currently spacecraft attempt to pre-emptively mitigate radiation events by using earth-based space weather forecasting. Gaining understanding and characterising radiation induced effects will be essential to real-time on-orbit mitigation. Single event effects (SEEs) arise from strikes of cosmic rays, protons or neutrons and they cause significant damage to electronics on board spacecraft. Characterising SEEs will be essential for outlining a procedure for the design and validation of radiation-tolerant electronic systems.

This proposed PhD will measure and characterise the types/intensity of radiation experienced in space through sensor instrumentation which can be implemented on-board spacecraft, and it will respond to measured results in real-time. Implementing a real-time response in space, using characterised radiation data, is a novel concept. Methods of radiation mitigation will be explored, as well as extensive environmental testing and simulation. The University of South Australia has endorsed this proposed PhD, with supervision by Associate Professor Ady James (primary supervisor) and Professor Ryszard Kowalczyk (co-supervisor). Dr James is the co-director of the Southern Hemisphere Space Studies Program and the Education Coordinator of SmartSat CRC. Dr James has worked on various space programs including Mars 96, Cluster II and Solar-B (Hinode). Dr Kowalczyk is the SmartSat CRC Chair in Artificial Intelligence, and he was the director of Swinburne Key Lab for Intelligent Software Systems and Head of Distributed AI Systems Research Group. In addition to the University of South Australia, the Australian National University has endorsed this PhD. Professor Mahandanda Dasgupta will co-supervise the PhD, allowing access to worldclass heavy-ion accelerator facilities. Dr Dasgupta is an experimental physicist and has been published in more than 80 journals, as well as being awarded a Queen Elizabeth II Fellowship and the prestigious Pawsey medal. Finally, this PhD is supported by SmartSat CRC, providing access to an alumni network of SmartSat CRC research partners and funding travel and PhD operational costs for this project.

The design and build phase of this PhD will occur at DST (Edinburgh) and the University of South Australia (Mawson Lakes). The testing phase will occur at the Australian National University (Canberra).


Project Leader:
Associate Professor Ady James, The University of South Australia

PhD Student:
Franke Agenbag, The University of South Australia/Defence Science and Technology Group


IPC Visualisation Task

On-board analytics

This project responds to a request from Defence Science and Technology Group (DSTG) to develop a visualisation of SmartSat CRC research activities to provide context for Defence capability managers. This initial activity will draw on conceptual work to show how Indo-Pacific Connector will deliver maritime domain awareness through space-based sensors and advanced communication technologies.


Project Leader:
Dr James Walsh, The University of South Australia


Robust Predictive AI: Advanced Satellite Hyperspectral Band Registration for Reliable Natural Disaster Event Prediction


This project will research a novel deep learning pipeline for achieving robust and reliable forecasting of natural disaster events with hyperspectral satellite imagery. The majority of the existing machine learning algorithms do not possess the ability to forecast the occurrence of natural disasters in advance and they are only able to detect their occurrence when the disaster event happens. To address this limitation, we propose novel research which leverages the spatiotemporal modelling capability of deep learning to forecast the occurrence of natural disasters in advance using a satellite onboard execution environment. The research will be conducted using the example use cases from the application area of bushfire event forecasting considering the practical significance to Australia. The developed framework will be deployable on a CubeSat Kanyini-type mission with Hyperscout-2 payload and will use onboard hardware to execute the algorithm.


Project Leader:
Professor Clinton Fookes, Queensland University of Technology