“You can’t hit what you can’t see” is a common sport idiom and was originally derived to describe baseball pitcher Walter Johnson’s fastball. Same goes for things with a more serious twist, like some of the millions of debris floating in Low Earth Orbit (LEO). Now, a team of researchers has developed a new imaging system that allows agencies and governments to closely track some of the debris that is cluttering LEO and potentially jeopardizing humanity’s future expansion to the stars.
This danger was first described by Donald Kessler in 1978 and is now commonly known as “Kessler Syndrome”. In such a scenario, the debris field surrounding the earth becomes so bad that it blocks access to (or from) space. To avoid such a fate, humankind will have to find ways to deal with space debris at some point. The hope that objects that disintegrate in LEO and burn in the atmosphere are not a viable damage control strategy.
Visual representation of Kessler syndrome.
Photo credit: NASA Orbital Debris Program Office
Such a mitigation strategy has so far proven difficult. Understanding and keeping track of how many objects are actually up there is one of the greatest challenges such endeavors face. Many parts are extremely small, spin very quickly, and move even faster. These combined properties make them very difficult to keep track of.
Traditionally, researchers have used one of two imaging techniques known as “single point cross-correlation migration” and “Kirchoff migration”, respectively. Single point migration has particularly poor resolution, which makes it difficult to determine the exact size and location of an object. However, it is not greatly affected by changes in the atmosphere. Alternatively, the Kirchoff migration is affected by atmospheric fluctuations, but offers much higher resolution.
Inquisitive droid youtube video about Kessler syndrome.
Photo credit: Curious Droid YouTube Channel
The novel approach developed by the researchers, known as rank 1 imaging, offers the best of both worlds. It has a similar resolution to the Kirchoff migration, but is nearly immune to atmospheric interference like the single point migration.
The secret of success of rank 1 lies in its algorithm. One of the hardest parts of tracking a orbiting LEO object is tracking it long enough to get a high resolution image. The main challenge for this tracking is the rotation of the object, which can affect even the best tracking algorithms due to the change in the reflectivity of the object.
Result of the various algorithms for the input data shown in the mission statement. Left: single point migration. Middle: Rank 1 algorithm, right: Kirchoff migration
Photo credit: Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
Rank 1 tries to estimate an object’s spin rate in order to understand its changing albedo. Brutally forcing spin estimates to fit the data might work, but it is time and computationally intensive. Instead, the rank 1 algorithm uses data captured by the object itself to inform its tracking algorithm of the direction and speed of its spin. With these estimates, tracking objects turns out to be much easier, which allows the algorithm to obtain a higher resolution image.
So far, the system has only been used for models and has not yet mapped an object directly in LEO. However, the algorithm performed extremely well with the model data provided, especially when compared to the two competing algorithms. With a little more development and a little time to track real objects, the rank 1 algorithm could become part of mankind’s arsenal to counter the growing threat of being locked out of space. If nothing else, at least we’ll see the threat.
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Visual representations of data fed into the three tested algorithms.
Photo credit: Matan Leibovich, George Papanicolaou, and Chrysoula Tsogka