Problem
A geostationary satellite changes its behaviour and the people responsible for the catalogue find out late, or not at all. The hard part is not seeing that the residuals moved — it is deciding whether that movement was a real manoeuvre, a mismodelled solar radiation pressure, or simply a bad track. Get that call wrong in one direction and you lose custody of the object; get it wrong in the other and you generate alarms nobody trusts.
Doing this at the scale of the GEO belt makes it worse. Analyst-led assessment does not scale to hundreds of objects observed nightly, and detectors trained on assumed dynamics degrade quietly as the population and operating practices change around them.
Capability
We build manoeuvre detection and pattern-of-life identification for geostationary and geosynchronous satellites, grounded in observational data rather than simulated behaviour. Three parts:
- A labelled optical dataset. The Optical Pattern of Life Analysis and Library (OPAL) dataset gives detectors something real to train and be evaluated against.
- Detection methods. Image-based and time-series transformer models for identifying manoeuvres, benchmarked against conventional residual-threshold approaches.
- A pattern-of-life framework. Integrating the above so that individual detections become a behavioural characterisation of an object over time, which is what an operator actually acts on.
Related characterisation work — satellite polarimetry through the UNSW Observatory, the ongoing PhD project of Asad Rizvi — supplies independent evidence about attitude and surface state to corroborate a detection.
The work is carried out by Michael Ling, research assistant on the NSW Space Research Network project, together with several students on supervised thesis projects covering manoeuvre modelling, track association and object characterisation.
Demonstrated result
- Competitively funded as a NSW Space Research Network Pilot Research Project for 2025–2026, Pattern of Life Identification for Geosynchronous Satellites Using Transformer-Based AI Foundation Models, in collaboration with Macquarie University and the University of Adelaide.
- A dedicated research position stood up to deliver the GEO pattern-of-life characterisation work.
- Method development presented externally, including transformers and deep learning for satellite manoeuvre detection at the UNSW AI Symposium (slides).
- Complementary manoeuvre-modelling work on factor graph optimisation for orbit determination with Gaussian approximation of impulsive manoeuvres, developed as a supervised thesis project.
External use
The project is co-delivered with Macquarie University under NSW Space Research Network funding, and it feeds the NSW Space Research Network white paper on Space Domain Awareness.
Next partnership opportunity
A manoeuvre and pattern-of-life assessment on your GEO or LEO time series: a labelled event list over the period you care about, and detection performance benchmarked against your current baseline, with false-alarm behaviour stated openly. See Partner with us.