Guidance & Atmospheric Inference for Station-Keeping

Europe’s operators of critical infrastructure and public services require continuous, high-resolution monitoring across extensive linear and urban assets. Stratospheric platforms can provide persistent, wide-area surveillance of infrastructure and ecosystems, bridging the sensing gap between drones and satellites. This experiment addresses the challenge of wind-driven drift in stratospheric platforms, which limits station-keeping performance. The work focuses on developing an uncertainty-aware guidance system that combines real-time telemetry with a multiscale Bayesian ensemble model. The technologies used include a multiscale Bayesian network trained offline using EuroHPC resources, parallel Monte Carlo simulations, and commercial cloud infrastructure. The goal is to reduce the station-keeping radius and increase mission success rates. The project will deliver two market-ready assets: an API providing probabilistic wind data and an Autonomous Flight Pack, enabling persistent Earth observation at sub-metre resolution for critical infrastructure monitoring.

Organisations involved: 

End-User: Involve Space s.r.l.

ISV: Bayesian Estimation for Engineering Solutions s.r.l.