A new space traffic management feature could save constellation operators millions of euros.
Neuraspace has introduced Machine Learning Prediction Plots, giving satellite and satellite constellation operators a tool for earlier collision avoidance planning.
Neuraspace is developing an advanced system for monitoring and preventing collisions in space.
The latest addition to Neuraspaces STM software, using artificial intelligence (AI), enables operators to decide several days earlier whether to proceed with the available data or wait for additional data before making preparations for a collision avoidance manoeuvre. It gives them the means to decide if the data is good enough to make a decision.
As a result, operators, in particular those operating constellations with dozens or hundreds of satellites, have more decision time and can extend their satellites lifespan by saving valuable fuel and avoiding unnecessary manoeuvres.
Chiara Manfletti, Director and Chief Operating Officer of Neuraspace, said: Neuraspace is the first STM company introducing Machine Learning Prediction Plots. Until now, no space traffic management tool was capable of making such an important forecast.
Satellite operators already receive a deluge of alerts, most of them false, and therefore perform many unnecessary but costly manoeuvres. A 300-satellite constellation may receive about 580 alerts, requiring human intervention and satellite manoeuvres, per year. With an emergency manoeuvre in LEO costing about 25,000, this adds up to a staggering cost of 14m per year. Saving some of these immense costs will make a huge impact.
Neuraspaces Machine Learning Prediction Plots calculate the path and forecast possible positions of the objects involved in conjunction at the time of closest approach (TCA).
With the existing huge amount of space debris and the expected growth of space traffic in LEO, the future evolution of the space industry will become uncertain, inefficient, and costly if not addressed, Manfletti concluded.
