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.

Satellite imagery provides immense potential for Earth observation applications. By training AI to analyse these images, we can monitor huge areas with minimal effort. However, there are many technical hurdles that need to be overcome to successfully use these images for application areas such as agriculture and ecology. Satellite imagery has much lower spatial resolution than ground-based images, and the higher resolutions satellite images (30cm) must be taken selectively as it is infeasible to capture the whole planet at that resolution. This leads to sporadic images being available for any site with only one image taken every few months, or several on the same day. To train an AI model on these images, we need to construct training sets of ground-based observations associated with positions in satellite images. The ground truth observations are also sporadic, and these combine to make training an AI algorithm on satellite images challenging due a lack of observations that are temporally aligned with images.

This project aims to develop sample efficient AI algorithms which require less ground truth observations to train accurate models. Both semi-supervised and unsupervised training methods will be adopted to train highly effective feature extractors using a minimum of labelled data. Highly successful semi-supervised learning algorithms, and unsupervised/self-supervised methods (which have achieved very high accuracy with just a few labelled examples or no labels at all (respectively) in generic image classification) will be customized and these methods extended for satellite images.

Distant clock synchronisation has many applications including telecommunications, Global Satellite Navigation Systems, emitter localisation, and phased array astronomy. Clock synchronisation requires clocks to tick at the same rate and to read the same time. Two procedures employed to ensure clocks read the same time are the Einstein method and the Eddington method.

The Einstein method involves sending a signal back and forth between two clocks and using the speed of the signal to synchronise the clocks.

The Eddington method involves synchronising the clocks next to each other prior to sending the second clock to its desired location.

Special relativity and general relativity must be accounted for in the Einstein and Eddington methods to ensure that the clocks run at the same rate. Furthermore, the Eddington method relies on the physical movement of a clock and is therefore not practical for space applications. This project will investigate the employment of entangled photon pairs in overcoming the limitations of the Einstein and Eddington methods.

Precision timing is a key capability underpinning the operational efficiency of society’s most critical infrastructure and is making new applications possible. Global Navigation Satellite System (GNSS) satellites carrying atomic clocks have contributed to delivering a global timing capability with high levels of accuracy and stability. The increasing demand for GNSS independent timing solutions, as well as the potential for optical clocks offering higher levels of timing accuracy and stability demands a study to address the potential applications and technologies required for success in these areas.

This project will investigate what new opportunities and resilience a compact, high accuracy clock for use on small satellites would enable for a broad range of precision timing applications.

Extensive research in Geostationary orbit (GEO) failure modes optimise resilience of single satellite systems. Resilience measures of Low Earth Orbit (LEO) constellations failure modes and their optimisations are less well understood.

This project concept will deliver a technology horizon scan for resilient, efficient and effective management of constellations of LEO satellites as well as a suggested roadmap for areas of focus for the SmartSat CRC.