Seeing the Unseen

Using synthetic tracking to observe dim objects in space

8 Ratings

You are charged with tracking dim objects, the thousands of fragments, spent rocket bodies, and inactive satellites spinning through unknown orbits around earth. Without knowing where to look, you would have to use countless telescopes to survey the entire sky and watch every possible trajectory for every possible object... a technical nightmare. But synthetic tracking, using post-processing algorithms and a select few telescopes, offers a much-needed solution.


The collision of two satellites creates a cloud of potentially dangerous space debris. Since the orbits of these new objects are not known, they cannot be tracked by conventional optical satellite sensors. They appear as dim streaks in images of the night sky or they may not appear at all. Not knowing where these debris objects are, we can't prevent them from damaging or destroying valuable spacecraft. We could solve this problem by engaging thousands of telescopes to track every potential object trajectory but this would be expensive and impracticable.


Space debris is a growing problem. Debris objects can't be avoided if you don't know where they are. According to NASA there are currently more than 20,000 objects larger than 10 cm in orbit around the earth and approximately 500,000 larger than 1 cm. These objects are moving with speeds up to 7 km/s and when they collide the relative impact speed can be up to 10 km/s. Consequently, the collision of an operational or manned satellite with even a small debris object can involve considerable energy which is likely to damage or destroy the spacecraft. Currently, these small dim objects can be seen if we know their orbit. Telescopes can take a long-exposure image while rate-tracking the object, causing it to appear as a single bright point. But if the orbit is unknown, the telescope image is insufficient.


Integrity Applications Incorporated discovered that we could use a data processing algorithm and a single telescope to identify dim objects rather than the thousands of telescopes previously needed. This "synthetic tracking" can use data gathered about a fixed area of the sky to chart multiple objects moving along different trajectories. This means that one physical telescope can act like many virtual telescopes, each tracking a different piece of debris. The synthetic tracking algorithm has been tested with data collected at the Air Force Maui Optical and Supercomputing (AMOS) observatory.

"As space becomes more crowded, the growing, large body of hard-to-detect objects (much of it debris) will impact the safety of our on-orbit assets. " — Keith Knox


Modern cameras are capable of taking images at video rates with very low noise. The telescope stares at a fixed location in the sky and takes a continuous stream of images. In post-processing, the stream of images is shifted along a hypothetical trajectory and combined to track an object moving on that trajectory. This process is repeated for many different hypothetical trajectories. If an object moving at one of these trajectories passes through the image then it will show up as a bright point in the image and be more easily avoided by operating spacecraft.


One problem with current ground-based optical satellite tracking is that the sensors can’t see satellites when the weather is bad. Our technique makes it possible to set up a geographically dispersed network of telescopes quickly and economically using commercial, off-the-shelf telescopes and cameras. With telescopes distributed over many locations, the odds are good that at least some of the locations will have good weather on any given night, improving our ability to track space debris.

Integrity Applications Incorporated (IAI)

Chantilly, VA

IAI is an engineering and software services company with a nationwide presence primarily supporting the intelligence community and other civil, defense and intelligence customers with a focus on Government space and intelligence surveillance reconnaissance systems activities.

Steven Long Steven Long

Steven Long

Principal Scientist

Channing Chow Channing Chow

Channing Chow

Senior Scientist

Leonard Baruela Leonard Baruela

Leonard Baruela

Software Developer


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