Slingshot Aerospace, the leader in AI-powered solutions for satellite tracking, space traffic coordination, and space modeling and simulation, announced it has been selected by AFWERX to support its Rapid Analysis of Photometric Tracks for space Object identification and behavior Recognition program. Under RAPTOR, Slingshot will use machine learning to track, analyze, and report on the behaviors of objects in low Earth orbit.

Initially, RAPTOR will be used to track and maintain custody of space objects of interest to U.S. Space Command, enabling timely reporting on events that could indicate an imminent satellite maneuver or mission change.

"Protecting our national interests demands the utmost focus on maintaining dominance and situational awareness in the space domain,” said Tim Solms, CEO of Slingshot Aerospace. “The Department of Defense must achieve comprehensive visibility and intelligence on covert and adversarial activities in space. RAPTOR delivers unparalleled awareness to safeguard critical assets, strengthen mission readiness, and uphold the security of our nation and its allies."

Vast amounts of photometric data from the Slingshot Global Sensor Network create digital signatures of space objects in LEO that Slingshot can identify, track, profile, and analyze. This photometric fingerprinting provides defense and intelligence agencies with a new set of tools to unlock applications, including:

• Tracking objects of interest: Slingshot’s systems can alert defense and intelligence agencies to satellite orientation changes and provide insights that can trigger an appropriate response to the situation. In the event of foreign launches by non-cooperative nations, RAPTOR can examine fingerprints of newly-launched satellites to determine their type, mission and whether further inspection is needed.
• Maintaining custody of objects: When custody of an object in orbit is lost, whether unintentionally or because of deliberate obfuscation, Slingshot can swiftly reacquire custody based on the object’s fingerprint. Additionally, if there is an intermingling of objects, the digital fingerprints help discern which object is which.
• Detecting anomalies: When objects behave unexpectedly or appear in surprising locations, Slingshot can compare the fingerprints of those satellites to fingerprints in its catalog and provide insights that help to determine the satellite type, capabilities, and/or mission. Fingerprints of all active satellites will be available as a reference when new satellites are deployed, helping characterize satellites with similar fingerprints.  

Slingshot currently maintains a catalog of approximately 14,500 active spacecraft and debris with its globally deployed network of optical sensors, which generate more than 4.5 million photometric observations each night. When analyzed, the resulting “light curves” create a unique digital fingerprint for each space object that can be fed into Slingshot’s Agatha AI model to identify changes like shifts in an object's orientation in space or its photometric signature.

“Establishing a comprehensive fingerprint database for all objects in orbit enables us to precisely identify an object’s nature and infer its potential mission objectives,” said Dr. Dylan Kesler, Vice President of Data Science, Slingshot Aerospace. “By applying machine learning across our network, we can identify unexpected behavior and use those insights to support our partners’ defense missions.”

RAPTOR program is part of a Small Business Innovation Research (SBIR) Phase II program administered by AFWERX, the innovation arm of the U.S. Air Force.

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