The rapid expansion of the orbital environment has produced an unprecedented challenge for space traffic management (STM). Commercial mega-constellations such as Starlink are deploying thousands of satellites, and active launches continuing to grow. The ability to reliably detect, track, and characterise individual objects is becoming increasingly critical. Traditional optical sensors provide rich photometric data but struggle with clutter and variable observing conditions, while passive radar offers complementary all-weather, 24/7 coverage. Fusing these modalities offers a path toward more complete characterisation. This project will develop and validate a multi-sensor fusion framework that combines a new computational optical imaging pipeline with the passive radar capabilities of the ICRAR-Curtin/Nova Systems Space Domain Awareness facility at Peterborough, South Australia.
A key innovation in this project is a novel optical image processing pipeline developed in collaboration with international partners, which applies image segmentation techniques, to our wide-field astronomical images. The pipeline uses adaptive segmentation and feature-enhancement processing. This project would use this to isolate satellite streak signals from complex, cluttered backgrounds, enabling reliable detection even in fields dominated by mega-constellation satellites.
Aim
Primary Aim: To develop and validate a multi-sensor data fusion framework that integrates optical light curves derived from the new segmentation-based imaging pipeline with passive radar detections from the Curtin/Nova facility as a testbed, enabling richer characterisation of space objects than is achievable from either sensor alone.
Secondary Aim: To establish methods for automated satellite population discrimination and pattern-of-life characterisation, with application to both routine in-orbit tracking and transient events such as uncontrolled re-entries, building on the existing multi-sensor detection infrastructure at the Peterborough facility.
Objectives
- Adapt and validate the segmentation-based optical pipeline for wide-field astronomical imaging, developing metrics for streak detection performance across varying sky brightness and satellite density conditions, including mega-constellation-dominated fields
- Develop algorithms to extract calibrated photometric light curves from detected satellite streaks and characterise their information content relative to physical properties (tumble rate, attitude, reflectivity)
- Design and implement data fusion protocols to combine radar, infrasound, and optical sensor data streams from the South Australian SSA testbed facility
Significance
The congestion of low Earth orbit is one of the defining challenges of the coming decade. Together, the fusion of passive radar and optical light curves positions Australia to contribute richer space object characterisation data than traditional single-sensor systems, supporting space traffic management applications. The project builds directly on ICRAR-Curtin’s proven expertise in both radio astronomy-derived passive radar and optical fireball network technologies, combining them with international expertise in advanced image segmentation.
Ideal Candidate
We are looking for a self-motivated PhD candidate with excellent organisation, problem-solving and project management skills. Candidates with strong quantitative skills, including familiarity with image processing, computing, data science are desired for this project. Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.
This project is open to Domestic applicants only .
Internship
Through this project you will also have an internship opportunity more information will be provided at a later date.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Dr Ellie Sansom at Eleanor.Sansom@curtin.edu.au
To formally apply submit an Expression of Interest to Dr Ellie Sansom during the Central Scholarship round (July 1st – July 31st 2026)