AFWERX backs Slingshot AI tech to monitor suspicious satellite activity

AFWERX backs Slingshot AI tech to monitor suspicious satellite activity
by Clarence Oxford
Los Angeles CA (SPX) Apr 03, 2025

Slingshot Aerospace has been selected by AFWERX to support the U.S. Department of Defense with advanced machine learning tools under its Rapid Analysis of Photometric Tracks for space Object identification and behavior Recognition (RAPTOR) initiative. This program aims to enhance situational awareness in low Earth orbit by analyzing the behavior of satellites and other spaceborne objects.

Through RAPTOR, Slingshot will deliver real-time insights into satellite maneuvers and other activity of interest to U.S. Space Command. These capabilities are expected to bolster the ability of defense personnel to detect covert mission shifts and prepare timely responses.

"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."

The core of the RAPTOR program relies on Slingshot's Global Sensor Network, which collects massive volumes of photometric data nightly. These observations are used to create distinct light signatures for space objects, a process known as photometric fingerprinting. These fingerprints help identify, track, and profile satellites, even in the case of deliberate concealment or unplanned loss of custody.

Applications of this technology include detecting orientation changes that may indicate a shift in mission, reacquiring lost objects in orbit, and distinguishing between intermingled satellites. In the event of foreign satellite launches, RAPTOR can evaluate their digital fingerprints to deduce purpose, design, and potential threats.

Currently, Slingshot tracks roughly 14,500 active satellites and debris fragments through more than 4.5 million nightly photometric observations. These are analyzed by its proprietary Agatha AI model to detect nuanced shifts in behavior or physical properties.

"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," commented Dr. Dylan Kesler, Vice President of Data Science at Slingshot Aerospace. "By applying machine learning across our network, we can identify unexpected behavior and use those insights to support our partners' defense missions."

The RAPTOR project is funded under the Small Business Innovation Research (SBIR) Phase II program led by AFWERX, which serves as the innovation hub of the U.S. Air Force.