Research Programs

Integrated Tactical Communications (ITC)

Dynamic Payloads – RF & Spectral

Secure, reliable, timely and resilient access to information is critical to success in any modern enterprise. This is especially true for military operations across the spectrum from humanitarian assistance and disaster relief (HADR) to battle in highly contested and congested environments.

Currently, the Australian Defence Force relies upon technology developed in the 1970’s to provide network connectivity for its arguably most at risk deployed forces. These systems have well known limitations yet there has been limited research into alternate technologies to support command and control and situational awareness for the tactical warfighter. The project seeks to identify and develop technology for advanced satellite communications as a long-term option to replace or augment these current high mobility satellite communications services.

The project builds on previous SmartSat funded research with a clear focus on three critical technologies to address this gap:

  1. Flexible and adaptive communications waveforms designed for the tactical user
  2. System wide network management to optimise resource allocation for capacity, coverage and resilience
  3. Reconfigurable, agile coverage using multi-frequency, multi-beam antenna arrays (pending future external funding)

This project will refine designs of the tactical communications waveform and initiate research into algorithms that optimise coverage and capacity of heterogeneous/hybrid satellite constellations including an initial demonstration implemented in software. The aim is to accelerate the technology development and understand risks in order to define a follow-on project, funded externally to SmartSat, that will develop a prototype space payload capable of integration with an experimental satellite. This subsequent phase will use the results from this project to inform the agile, multi-beam, multi-band phased array design and the development of initial user terminals. It is expected maturing this technology to the point it can be demonstrated in space will cost $5M – $10M and take three years. This is beyond the resource available from SmartSat so this project will include the
submission of a bid for Defence innovation/prototyping funding to support maturation of the critical underlying technology from TRL4/5 to TRL8.

The target technology demonstration and experimentation program for this research is the Defence STaR Shot for Resilient Multi-mission Space (RMS). The demonstration will showcase a game-changing approach to the provision of resilient satellite communications to the tactical warfighter.

Note: Within this project, tactical communications means systems supporting high levels of user mobility which requires the use of very small aperture terminals (e.g. handheld) and the ability to operate over complex RF propagation channels.

P1.30

Project Leader:
Jeff Kasparian, SmartSat CRC

Participants:

SatPing – a tracking beacon for spacecraft

Debris Avoidance

Immense growth in the use of spacecraft and the orbital debris population is driving an urgent need for effective space traffic management (STM) and responsible use of space. Currently, orbital knowledge (position and velocity) is primarily obtained by ground-based remote sensing (e.g., radar, optical, etc.) which results in significant position and velocity uncertainty.

These uncertainties complicate conjunction assessments and subsequent collision avoidance manoeuvres.
The goal is to develop a self-sufficient, bolt-on system that can be used for active and real-time orbital knowledge (position and velocity) from the spacecraft.

A design approach to improve Space Traffic Management (STM):

  • Like an ADS-B for Spacecraft (analogous to air trafic management moving from radar to active GPS broadcast)
  • Chip continuously broadcasts a ‘ping’ to listeners
  • Determine position and velocity through RF geolocation (and/or GPS)
  • Low SWAP and no interface/interaction with spacecraft (‘bolt-on’)
  • Simple processor + Transmitter + Battery + Mini Solar array (+ GPS)
  • Provides information even for dead spacecraft or rocket bodies

We propose a short-term scoping study to evaluate the feasibility of this onboard beacon that can be used to provide high-precision, real-time orbital information – assessing ITU and RF broadcasting limitations, technical extent of existing capabilities and connecting with relevant Australian (and international) entities and regulatory bodies to advance the concept to implementation.

P2.50

Project Leader:
Associate Professor Shannon Ryan, Deakin University

Participants:

SCARLET-⍺: SpaceCraft Autonomy and Onboard AI for Next Generation Space Systems

SCARLET Lab

 

Spacecraft autonomy has been recognised as a key enabler of the next-generation space systems that aim at increasing responsiveness and continuity of space-based observations, covering large areas with higher resolutions, minimizing communication and data access latencies, and reducing costs of both the space and ground segments.

Spacecraft autonomy encompasses onboard autonomous decision-making capabilities that enable the space segment to continue mission operations and to survive critical situations without relying on ground segment intervention. It relates to all aspects of spacecraft operations, including continuous mission planning and execution on board, real-time spacecraft control outside ground contact, maximisation of mission objectives in relation to the available onboard resources and capabilities of other spacecraft, and system robustness in presence of on-board failures and external uncertainties.

This project aims at addressing the above requirements by developing novel concepts, methods and technologies to provide new AI-based spacecraft autonomy capabilities for the next-generation space systems, such as dynamically networked formations of heterogeneous satellites. It focuses on high impact areas of spacecraft autonomy and onboard AI as identified and prioritised with the industry and defence partners, including:

  • WP1: Onboard processing and actionable intelligence
  • WP2: Small spacecraft and constellation resilience
  • WP3: Dynamic optimisation of constellation resources
  • WP4: Real-time tasking and resource allocation

The output of this project is a set of autonomous algorithms, demonstrating their capability through software simulations with use-cases provided by the industry partners.

This project will leverage and contribute to the IPC Capability Demonstrator with an aim to be demonstrated on DST RMS STaR Shot as a pathway to commercialisation of the developed technology solutions for industry partners.

P2.52

Project Leader:
Professor Ryszard Kowalczyk, University of South Australia

Participants:

An empirical and dynamic tool for prediction of forest fire spread using RS and ML techniques

EO Analytics

Australia has a total of 134 million hectares of forest, which is equivalent to 17% of Australia’s land area. Of this total forest area, determined as at 2016, 132 million hectares (98%) are ‘Native forests’, 1.95 million hectares are ‘Commercial plantations’, and 0.47 million hectares are ‘other forest’. Australia has about 3% of the world’s forest area and globally is the country with the seventh-largest forest area. The Australian ecosystem is shaped by fires for over 70 000 years and each ecosystem has its fire regimes. Additionally, fuel management and predicting flammable areas are the key to managing wildfires. These factors play a vital role in resource allocation, mitigation and recovery efforts. Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Therefore, it is important to know and understand the behaviour of fire ignition and spread so that fire management agencies can prevent and mitigate wildfires. This project aims to develop a tool to predict Forest Fire Spread using Machine Learning approach and Weather Research and Forecasting with fire spread model (WRF-SFIRE).

In this project, fire risk probability mapping, Prediction of fire points, and fire spread modelling will be carried out for the flammable forest areas in Australia. The fire risk probability model will be prepared by using the two-step Analytic Hierarchy Process (AHP) approach. The fire risk probability model will also be used for cross-validation of Support Vector Machine (SVM) model outputs. Then the Prediction of fire points will be done using SVM model (Linear kernel, Polynomial kernel, Radial kernel, sigmoid kernel) taking elevation, slope, aspect, Soil Moisture Layer (SML), Land Surface Temperature (LST), vegetation type layers for training the data set. Then the Weather Research and Forecasting (WRF) model will be used for obtaining meteorological data for the input of fire spread model using the Global Forecast System (GFS) as source data. After that, the fire spread path will be traced for various recent past fire event. The additional variables will be used for spread modelling other than SVM are, canopy cover, wind, temperature. The Cellular Automata (CA) fire spread model will setup in any two different locations of Australia.

P2.19s

Project Leader:
Sanjeev Kumar Srivastava, University of Sunshine Coast

PhD Student:
Harikesh Singh

Participants:

Onboard Hyperspectral AI: Calibration, Panoptic Segmentation, Fine-grained Analysis, and joint space-ground inference

SCARLET Lab

This project will develop brand new capabilities for onboard AI processing and analysis of hyperspectral imagery on smart satellite platforms. In particular, the project will tackle the key modules of calibration, coarse and fine-grained segmentation, and joint space-ground inference of onboard AI processing of hyperspectral data. New capabilities in these areas will transform the ability of a satellite to automatically make sense of the rich and multidimensional spectral modalities in an end-to-end manner onboard the satellite itself. This will create new opportunities to enable accurate, efficient, and reliable automated detection and classification of natural phenomena and human activities over a wide area on Earth.

At the heart of the project, the research team will develop a novel multi-task learning framework for hyperspectral data. This framework will be employed to create a Panoptic Segmentation network: an approach which unifies object detection, semantic segmentation and instance segmentation in a single network to simultaneously predict a dense pixel-level segmentation across multiple spectral channels from space. In addition to this, the project will develop a lightweight deep learning based atmospheric correction network which can also be deployed onboard; and explore how joint learning between satellite and ground-based sensors can be used to support the inference of detailed information in areas not covered by ground sensors.

This Phase 1 project will develop a proof-of-concept demonstrator system, developing the key techniques for later optimisation and integration to run onboard the Kanyini satellite (SASAT1).

P2.34

Project Leader:
Professor Clinton Fookes, Queensland University of Technology

Participants:

Cyber Security and Resilient Low Earth Orbit Satellite Operations: Development of Cyberworthiness using a Digital Twin Approach (CY-JAR)

Trusted Autonomous Satellite Operations

Technical failures on satellite systems can be difficult to attribute to cyberattacks. Often, there is insufficient information to support root cause analysis and space vehicles are unable to provide robust response options to cyber-attacks.

This project aims to support the development of cognisant satellites which are context aware and capable of increased mission resilience in the face of a contested cyber operating environment.

P2.37

Project Leader:
Professor Jill Slay, University of South Australia

Participants: