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.

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.

Astrodynamical simulations provide a crucial input to space mission planning and operations. Interactive visualisation of mission configurations, particularly for multi-spacecraft constellations or formation-flying scenarios, plays an important role in both understanding options and communicating outcomes to a variety of end users or audiences.

This project will investigate current state-of-the-art software for mission simulation and visualisation. This includes an evaluation of a suite of commercial and open-source options, considering both quantitative metrics (performance benchmarks, accuracy of orbital calculations) and qualitative factors (usability, flexibility, licensing costs and platforms for delivery).

The outcomes will comprise improved understanding of the suitability of astrodynamics simulation and visualisation software (shared as a white paper and/or research publication), identification of new opportunities for research and development, and improved understanding of the needs of end users from various SmartSat partners with the goal of advancing Australia’s capabilities in mission simulation, operations, and space situational awareness activities.

A literature/technology review on AI architectures for On-board Processing and related hardware aspects (GPU-based, FPGA, SoC etc).

Currently, space situational awareness (SSA) highly depends on ground-based capabilities limiting the accuracy of tracking objects to 10cm. This project will explore possible technological demonstration that utilizes International Space Station/ CubeSat payload for SSA alongside its efficiency and need for long-term space sustainability. The capability shall be achieved through machine learning as a part of the onboard computer.

This project aims to develop a novel distributed radio frequency processing capability for deployment across space-based platforms. The research will focus on elaborating a machine learning (ML) model that can effectively detect and classify conventional and low-probability-of-intercept (LPI) radar signals. The model will dynamically adapt its structure to the collected signals, producing calibrated confidence estimates and learning from misclassified in-distribution samples. To enhance autonomy and resilience to mission contingencies, the neural network will perform asynchronous, distributed training featuring
interruptible and resumable cycles.