Neuraspace Strengthens its Space Traffic Management Solution with EISCAT Partnership
EISCAT Svalbard Radar Credit: Craig Heinselman
Widening its partner base, Neuraspace has joined EISCAT Scientific Association as an affiliate institution. EISCAT Scientific Association has been providing access to high-latitude incoherent radar scattering facilities and other instrumentation for scientific, non-military purposes since the 1980ies.
This strategic partnership gives Neuraspace, a European-born global leader in space traffic management (STM), access to radar observation time for monitoring space objects. It will also provide advanced ionospheric and atmospheric measurement data, in preparation for using the next generation of the incoherent scatter radar facility EISCAT_3D.
Using EISCAT’s legacy radar data, which complements Neuraspace's optical data, will enable the company to offer a more comprehensive view of the space environment and improve automated operations, manoeuvre suggestions and decision-making for Neuraspace customers.
"Access to EISCAT data is a game-changer for Neuraspace," said Chiara Manfletti, CEO of Neuraspace. "We can develop more robust methodologies and algorithms to correlate information from various sources. This ultimately leads to actionable intelligence for automated operations incorporating space traffic management. EISCAT’s expertise and commitment to scientific progress aligns perfectly with our mission. Their future endeavours, such as EISCAT 3D, further solidify this partnership's potential.”
Partnering with EISCAT also allows Neuraspace to collaborate with the wider community of experts, fostering innovation in STEM research and development.
Dr Thomas Ulich, head of science at the EISCAT Scientific Association, said, “Maintaining accessible orbits is critical to societal functions. Our data will empower Neuraspace to refine object tracking and optimise space operations.”
In particular, EISCAT’s historical data sets of catalogued (white-listed) space objects will be instrumental for Neuraspace. This information will enable Neuraspace to develop new machine-learning algorithms. This will also improve the taxonomy of resident space objects, better predict their behaviour and identify patterns.
Insights collected from this data will be fed into Neuraspace's STM solution, leading to more informed choices for its customers. Neuraspace already predicts the evolution of object uncertainty up to five days ahead of the time of the closest approach.
The EISCAT Scientific Association is in the final stages of developing the next-generation incoherent scatter radar system in the Arctic called “EISCAT 3D”. The first test measurements with EISCAT 3D are expected to be carried out in the coming year.