Precision timing is of vital importance to our modern society. Its most high-profile application is seen in daily use by most of the world’s population though Global Navigation Satellite Systems (e.g. GPS, Galileo), which generates trillions of dollars each year in economic benefits around the globe.

Other applications for precision timing are emerging within satellite constellations where highly accurate satellite position and timing information may be required. Such information is crucial for: intelligent space systems that aim to produce high-resolution monitoring of Earth by combining data from multiple low-resolution sensors, or next-generation GNSS and satellite communication constellations which are more immune to spoofing, offer higher accuracy, and could lead to a sovereign capability for Australia.

This project aims to demonstrate a next generation timing reference for spaced-based applications. The project will focus on design optimisation for small satellites (typically about 1m3, 100-200kg) as well as initiating an understanding of the trade-space between performance and SWaP for satellite clock designs.

It has become a concern in recent years that the low Earth orbits are turning into a congested and contaminated environment with the proliferation of orbital debris. So far, approximately 34,000 objects larger than 10 cm in diameter lie in this region, about 900,000 pieces of debris 1-10 cm, and the number of objects smaller than 1 cm is estimated to be up to 128 million. With the development and commercialisation of small satellites, the small satellite market is expected to reach $15,686.3 million by 2026. Any impact or collision of space debris with the operational satellites can jeopardise or even end their life, yield significant loss to the space economy, and trigger the so-called Kessler Syndrome which refers to the possibility that collisions will create more debris collisions.

The University of Sydney are developing space-based optical sensors, including telescopes, hyperspectral imager and wide field of view star tracker under the ARC training Centre for CubeSats, UAVs and the Applications (CUAVA). These sensors are initially developed for other purposes like Earth observation, astronomy and attitude and orbit determination. This project however will look at the feasibility of applying these sensors for space objects detection and tracking in orbit.

Intelligent autonomous navigation capability for space exploration includes autonomous approaches to small bodies, rendezvous, landing and surface operations such as surveying and sampling. These manoeuvres and scientific experiments will be performed by robotic craft such as satellites. The proposed area of research intends to investigate vision-based navigation on a satellite to assess the prospective autonomy for space exploration.

The proposed research will focus on the development of AI-based trusted autonomous system for on-board data processing and space/ground segment co-evolution in the Distributed Satellite Systems (DSS) architectures.

To fulfil mission objectives satisfactorily, many CubeSat-based applications require precise stabilisation of the CubeSat platform during orbit. For example, observing a small distant space object, (re)detecting small targets or fine-grain changes over a large terrain of the Earth’s surface, and establishing long-range communication links. However, in part due to their small size, CubeSats inevitably suffer from jitter during orbit, which prevents a high degree of stability.

This project seeks to research and develop an ultra-fine attitude determination and control system (ADACs) for optical remote sensing (Earth Observation and Space Situational Awareness) and optical communications from small satellite platforms.

This project seeks to automate the identification of higher order patterns in ISR (Intelligence, Surveillance and Reconnaissance) sensed detections along with establishing normalcy. The intention is for significant changes from normalcy – anomalies – to be reported to operators as alerts requiring human assessment, decision, and action. In addition, the rationale of the alerts will also be computed and presented in a transparent way to instil user confidence in the results.

Two novel aspects for this project are: (1) the use of multiple strategies for pattern detection, including deep learning and advanced statistical modelling (e.g., Bayesian Computation); and (2) the incorporation of a Pattern Question Answering (PQA) capability to enable intuitive interaction and interrogation of the reported patterns for their rationale. PQA will build on and generalise existing capabilities in Visual Question Answering (VQA) in the fields of Artificial Intelligence and Machine Learning.

Specific application domains will be considered to support the development and demonstration of capability, including domains such as maritime traffic, space situational patterns, and land use patterns.