Gettyimages.com / Yuichiro Chino
Slingshot Aerospace secures Air Force contract to ‘fingerprint’ objects in space
The company is tasked with using artificial intelligence to analyze 4.5 million nightly observations from a global sensor network that tracks 14,500 satellites and other objects in space.
Slingshot Aerospace has been chosen by the Air Force's technology acceleration arm to support a program for identifying and tracking various objects in space.
The company specializes in artificial intelligence solutions for satellite tracking, space traffic coordination, and space modeling.
AFWERX has awarded Slingshot a Small Business Innovation Research phase II contract worth up to $1.2 million. The award will support the Rapid Analysis of Photometric Tracks for Space Object Identification and Behavior Recognition program, known as RAPTOR.
Space Command is the first customer and it will use RAPTOR to track events that could indicate the need for an immediate satellite maneuver or mission change.
"Protecting our national interests demands the utmost focus on maintaining dominance and situational awareness in the space domain,” Slingshot's CEO Tim Solms said in a release.
The Defense Department needs to comprehensive visibility and intelligence on adversaries’ activities in space, he said. RAPTOR can deliver that visibility.
Slingshot ingests vast amounts of photometric data through its global sensor network. The data is then used to create digital signatures of space objects in low-earth orbit.
Slingshot uses those signatures to identify, track and profile the objects.
The information helps in tracking objects of interest, maintaining custody of objects and detecting anomalies.
The company maintains a catalog of 14,500 active spacecraft and debris. Its global sensor network captures 4.5 million photometric observations each night. Slingshot “fingerprints” each object by analyzing the light curves each space object generates.
The company’s AI then works to monitor the fingerprints to track changes in the object’s orientation 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 Dylan Kesler, vice president of data science at Slingshot. “By applying machine learning across our network, we can identify unexpected behavior and use those insights to support our partners’ defense missions.”