Last week, PSAI demonstrated our expanded AI toolset for space domain awareness to an audience of military analysts, officers, data scientists, and interested contractors at the SDA TAP Lab’s Demo Day. 

We had capped lab Cohorts 4 and 5 in October and January by showing that our AI and machine-learning methods can generate deeply-detailed capability profiles of thousands of spacecraft from millions of public data records.

During Cohort 6 – as shown in last week’s demo – we developed an image analyzer, adding a significant new capability to the lab’s work helping U.S. Space Force Guardians identify and assess unknown, concealed, or hostile spacecraft. 

Our newest tool can ingest a photo of a payload – either from orbit or in a pre-launch clean room – and perform a pixel-by-pixel analysis of its potential capabilities. 

The tool estimates the spacecraft’s size, shape, and mass. It counts and estimates the size of antennas and solar panels. And from these and other visual clues, it can infer onboard capabilities including sensors, cameras, radio equipment and frequencies, and the presence and orientation of thrusters. 

By deploying this tool in their analytical processes, Guardians can gain context for the decisions they must make when interrogating targets for evidence of camouflage, concealment, deception, and maneuvers; What sensors are on board? How powerful might its solar panels, batteries, and thrusters be? Can it take high-resolution photos, jam our radio frequencies, or fire an energy weapon? 

While this tool is in its infancy, we hope to refine and grow its capabilities further when Cohort 7 kicks off later this month. 

Watch this space.