SmartSat is integrating the capabilities from the priority areas set in SmartSat’s Technology Roadmap across three primary research program areas as below.

The ever-increasing number of satellites being launched into space will pose significant challenges in tracing satellites, avoiding collisions in an increasingly crowded space and integrating different technologies and systems.  As satellite technology becomes physically smaller and are deployed in constellations, increased opportunities exist for significant processing and Artificial Intelligence (AI) techniques to be out on-board satellites so that some advanced analytics are carried out on-board satellites to enhance the efficiency and effectiveness of data gathering and analysis.

See projects in Advanced Satellite Systems, Sensors and Intelligence projects below:

Topics include:

  • MBSE & Digital twins of small satellite systems
  • Autonomous, cooperative satellite formations
  • Artificial Immune Systems in satellite swarms
  • Trusted Autonomous Formations
  • Self-healing satellite systems
  • Agile & resilient satellites
  • Satellite system & data security
  • Advanced pointing & maneuvering
  • On-board machine learning modules
  • Advanced adaptable payloads
  • HgCdTelR Optoelectronic sensors
  • Quantum sensors

Autonomous navigation of satellites for space exploration

Advanced Satellite Systems, Sensors and Intelligence

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.

P2.02s

Project Leader:
Professor Xiaofeng Wu, The University of Sydney

PhD Student:
Anne Bettens, The University of Sydney

Participants:

Artificial Intelligence for Distributed Satellite Systems Autonomous Operations

Advanced Satellite Systems, Sensors and Intelligence

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.

P2.13s

Project Leader:
Professor Roberto Sabatini, Royal Melbourne Institute of Technology (RMIT)

PhD Student:
Kathiravan Thangavel, Royal Melbourne Institute of Technology (RMIT)

Participants:

Ultra-fine Attitude Control via Event-based Star Tracking and Piezoelectric Stabilisation

Advanced Satellite Systems, Sensors and Intelligence

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.

P2.01

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

Participants:

Trusted AI Frameworks for Change and Anomaly Detection in Observed ISR Patterns

Advanced Satellite Systems, Sensors and Intelligence

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.

P2.11

Project Leader:
Matthew Roughan, The University of Adelaide

Participants:

The Application of Artificial Intelligence for Satellite Enterprise Management

Advanced Satellite Systems, Sensors and Intelligence

This project aims to focus on existing SATCOM enterprise architectures to quantify the performance gains achievable through the incorporation of Artificial Intelligence techniques.

P2.07

Project Leader:
Professor Jinho Choi, Deakin University

Participants:

Super resolution Mosaic Infrared Focal (SMIRF) Sensor

Advanced Satellite Systems, Sensors and Intelligence

When a satellite stops communicating it is difficult for an operator to determine the cause or nature of the failure and to determine an appropriate response.

Failures can be caused by many events including space based sub-system failures, impaired access to communication spectrum or spacecraft loss due to a collision with space debris. This project aims to advance the concept of a small, system independent suite of sensors and processors feeding information into an Artificial Intelligence (AI) based interpreter that will identify the potential jeopardy of the platform as well as propose an appropriate response.

This work is an important precursor to the development of cognitive satellites – satellites that are “context aware” of their operating environment and are able to independently self-configure to achieve increased mission resilience in a hazardous environment.

P2.22

Project Leader:
Mark Ramsey, SITAEL

Participants: