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).

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.

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.

This project aims to develop a distributed testbed for satellite testing, more commonly known as FlatSat integration testing. This phase of the project will develop node devices and a minimal software ecosystem that will enable geographically separated satellite hardware to undergo integration and hardware in the loop testing over the internet.

The aim of this project is to research, develop and test goal-oriented algorithms and software that will grant a spacecraft autonomous capability to undertake its mission robustly and adaptively in real-time. The activities will focus on coupling optimisation and machine learning techniques to orbital and sensing prediction models, such that when sensing data is obtained in real time the next most optimal action can be determined.

The autonomy will be experimentally tested using the DSTG Buccaneer Main Mission (BMM) spacecraft scheduled to launch in 2023. BMM features the MANTIS payload with a controllable, deployable arm for self-inspection imaging. The project output will be a set of algorithms, methodologies, and approach to grant the spacecraft the ability to take an optimal image of itself against a backdrop of Australia in real-time. The outcomes will be learnings of the relationship between on-board and off-board autonomy that will be applicable to other spacecraft missions.

Space is becoming increasingly congested, contested, and competitive due to increased economic access to orbit, launches of distributed satellite systems, and conjunction events due to debris and conflict. For future satellite missions, on-board algorithms that can detect, track, identify, and characterise events and hazards that occur on-orbit will be required for space activities. One activity, autonomous maneuvering, is yet to be fully realised with the latest developments in artificial intelligence. Ability to test these
algorithms on the ground is limited and the capability does not exist in Australia. This project will commence with the development of a facility, and develop novel machine perception, navigation, guidance, and machine learning algorithms on space-based hardware.