Thousands of both natural and man-made objects currently orbit the Earth, ranging from debris the size of a grain of sand to entire satellites. In fact, by 2050, the number of man-made satellites orbiting is predicted to reach 50,000. With the immense importance of these satellites for communication, navigation, and so much more, it is essential that they are protected from any damage. However, this is easier said than done. A piece of debris the size of a paint fleck could be enough to damage a satellite at the speed these objects travel at. And this problem will only continue to get worse, due to an issue known as Kessler Syndrome.
Proposed by Donald Kessler in the 1970s, Kessler Syndrome is the idea that ‘space junk’ moving at high speeds in low Earth orbit has a tendency to collide and break into smaller pieces. These collisions increase the number of space junk bits which inevitably create more collisions. It creates a positive feedback loop of more pieces, more collisions, and therefore more pieces. This “cascade of collisions,” as NASA calls it, means that the more debris is in space, the worse the problem will get.
Researchers at Stevens, Professors Rajarathnam Chandramouli and K.P. Subbalakshmi, worked on this problem. The team developed artificial intelligence used to optimize how sensors detect space junk that is orbiting Earth and focused on predicting collisions before they happen. The professors won first place overall universities in the 2021 Space Force’s Hyperspace Challenge for their work. The challenge is designed to accelerate innovation in the field of space, and create relationships between the government, businesses, and universities in this field. The Stevens Team also won $25,000 with this award.
This AI technology was originally developed to switch radio bandwidths for first responders when airways get too crowded during emergency situations. It has now been repurposed for space. Currently, the US Department of Defense maintains a catalog of the debris currently orbiting Earth, as well as sensors and detectors for tracking where those objects move. Professors Chandramouli and Subbalakshmi’s technology works by deciding which radars should look at which areas of space and which objects in real time. These then observe the object’s path, as well as any potential deviations from that path. The program also rewards successfully predicted patterns and avoids unsuccessful ones, optimizing itself in real time. This means that its effectiveness increases as it is used more, which is vital when it comes to protecting satellites.
Their detector system was 60% more efficient than the current systems for detecting space objects. The team points out that their next steps will be adapting the algorithm to be able to make real time assessments of any characteristics of the objects themselves as well as any unique parts of its path.
With the future use of space and talk of space travel increasing dramatically, the importance of merging technology with development cannot be understated. Programs such as these mark an important step forward in accurately predicting the movement of objects, as well as potentially avoiding dangerous collisions with all-important satellites.
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