, and other national and international bodies. This dataset must be diverse and include orbital scenarios, trajectories, illumination conditions, and precise data on the geometry, material distribution, and attitude motion of all orbiting objects at all times.In short, scientists would need a robust database of all human-made objects in space for comparison to eliminate false positives.
They then used a machine learning-based classification system to associate the probability of detecting a combination of materials with a particular class. With the pipeline complete, said Vasile, the next step was to run a series of tests, which provided encouraging data: “We ran three tests: one in a laboratory with a mockup of a satellite made of known materials. These tests were very positive. Then we created a high-fidelity simulator to simulate real observation of objects in orbit. Test were positive and we learnt a lot. Finally we used a telescope and we observed a number of satellites and the space station. In this case, some tests were good some less good because our material database is currently rather small.
In their next paper, Vasile and his colleagues will present the attitude reconstruction part of their pipeline, which they hope to present at the upcoming