Scientists Use Lasers to Find Space Junk

Chinese scientists have used lasers to accurately identify space debris in Earth's orbit.

The team applied a specially developed set of algorithms to laser-ranging telescopes, which enabled them to identify space junk more effectively than previous techniques, according to a study published in the Journal of Laser Applications.

"After improving the pointing accuracy of the telescope through a neural network, space debris with a cross sectional area of one meter squared (10 square feet) and a distance of 1,500 kilometers (932 miles) can be detected," Tianming Ma, an author of the study from the Chinese Academy of Surveying and Mapping, Beijing and Liaoning Technical University, Fuxin, said in a statement.

According to NASA, orbital debris are any man-made object in orbit around the Earth that no longer serve a useful function. Debris can include non-functional spacecraft, abandoned launch vehicle stages, and other mission-related space junk.

It poses a significant risk to astronauts and spacecraft in orbit above the Earth as they travel very fast—up to 18,000 miles per hour, or faster than a bullet.

Worryingly, there are estimated to be many millions of tiny debris pieces in low-Earth orbit, and tens of thousands larger than a a softball, the space agency said. Fortunately, there have been surprisingly few collisions.

Laser ranging technology has previously been used to detect space junk. However, these systems have their limitations when it comes to accurately identifying small, fast-moving pieces. In fact, previous laser-ranging methods have only been accurate to about 0.6 miles.

In an attempt to overcome the inaccuracies inherent in laser-ranging techniques, the Chinese team used so-called neural networks to improve the effectiveness of their telescope system.

space debris
Stock photo: Artist's illustration of space debris. iStock

Neural networks are computing systems which are inspired by biological networks in the brain. They can learn to become better at tasks without being given a specific set of rules to follow—what's known as a "machine learning."

In their study, Ma and his team used two different neural networks to help identify space debris with the laser-ranging telescope. They then tested this method against more traditional techniques at the Beijing Fangshen laser range telescope station.

According to the team, this is the first time that neural network have been used to significantly improve the pointing accuracy of a laser-ranging telescope. They say that the latest findings could have significant implications for maneuvering spacecraft in orbit.

"Obtaining the precise orbit of space debris can provide effective help for the safe operation of spacecraft in orbit," Ma said.