Space junk is an unrecognized problem that we are facing outside this world. Not only are we polluting our mother planet, Earth, but it is also evident that we also negatively contribute to waste outside our planet.

Contribution of Human Kind to Space Litter Debris

Contribution of Human Kind to Space Litter Debris - THESIS.PH

There are many satellites sent in outer space, which unfortunately never come back. Mostly, it is because it turns into infinite fragments from disintegrated satellites, both living and dead.

Given that their speed is faster than 23,000 miles per hour, collisions between these satellites damage other satellites sent outside the planet. These particular collisions eventually leave dents and sometimes even pits in satellites and other objects.

We contribute a significant amount of space litter. Apart from more than 500,000 small debris that add to the litter, there are more than 23,000 fragments, bigger than 4 inches, that are caused by humans.

Some of the events that caused these numbers are the following:

  • In 2006, a chip popped out from the International Space Station’s window after a small particle collided with its vicinity.
  • China, India, and the U.S. tested their anti-satellite missile by blowing their satellites from outer space.
  • On March 27, 2019, India created another 400 pieces of debris after targeting a low-altitude satellite named Microsat-R.

Overcoming Challenges From the Space Debris Dilemma

Overcoming Challenges From the Space Debris Dilemma - THESIS.PH

Among more than 50 measurement and tracking networks and global laser observation stations, scientists widely utilized laser ranging technology to locate space fragments.

Using this technology, scientists discovered that the problems are due to:

  • poor prediction accuracy
  • small size
  • no reflection prism on the surface of debris

As the debris in space grew smaller from centimeters down to millimeters, it is now a challenge for our experts to precisely locate the waste from these debris fragments.

Tracking Networks and Global Laser Observation Stations - THESIS.PH

To address these challenges, scientists utilized a method where they made corrections on the laser ranging systems’ telescope pointing errors. They established a model for pointing correction and enhanced their hardware equipment. This infamous procedure became more widely utilized.

Outlook for a New Approach Using Deep Learning Techniques

Outlook for a New Approach Using Deep Learning Techniques - THESIS.PH

A team of Chinese researchers from Fuxin’s Liaoning Technical University and Beijing’s Chinese Academy of Surveying and Mapping used deep learning techniques to improve space junk identification accuracy to develop effective strategies for maneuvering space.

They aimed to analyze four different models of telescope pointing corrections, to pick the one with the highest accuracy. Among 95 stars observed, 22 was the highest predicted number from a BP neural network model. This result is better than the three other models, namely:

  • mount model
  • spherical harmonic function model
  • basic parameter model

It turns out that deep learning techniques were effective in:

  • assisting scientists in improving both accuracy and connection speed
  • manipulating satellites to avoid potential collisions
  • drastically improving object spotting

Most importantly, these various laser techniques could be helpful for scientists in detecting satellite debris fragments, removing space junk, and bringing it back to our planet to finally abolish risks for traffic when launching and maintaining satellites in outer space.

Do you have a better idea than using the laser technique to clean our outer space from man-made junk?

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