ROME, N.Y. – U.S. Air Force researchers are asking industry to use artificial intelligence (AI), machine learning, and machine inferencing in developing enabling technologies in a new high-performance computing (HPC) system for next-generation target tracking that uses many data sources.
Officials of the Air Force Research Laboratory Information Directorate in Rome, N.Y., issued a presolicitation (FA875024S7004) on Tuesday for the Advanced Tracking Architecture Using AI (ATA-AI) project.
From industry, the ATA-AI project seeks algorithms and applications for 3D pixel, vector, and point cloud processing and accelerations on high-performance computing system; and ways to use AI and machine learning for identification, classification, and pattern learning from signals and image intelligence.
The project also asks industry to develop ways to process signals from satellite and inertial navigation, RF identification trackers, or telematic-based data into traffic tracks that can measure how an adversary uses lines of communication.
In addition, the ATA-AI target tracking project asks industry to develop ways of processing cell phone, satellite and inertial navigation, RF identification trackers, accelerometers, altimeters, and personal fit devices into graphic data that first responders can use to locate people in disaster areas based on last known location from their personal devices.
For now, Air Force researchers are asking industry to submit white papers. Those submitting promising white papers will be invited to submit formal proposals. Several awards are expected for this program that will be worth about $99 million over the next four years.
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Companies interested should email white papers to the Air Force's Carolyn Sheaff at [email protected] and Peter Rocci at [email protected] no later than 30 Nov. 2028.
Email technical questions or concerns to Carolyn Sheaff at [email protected] and Peter Rocci at [email protected]. Email business questions to Amber Buckley at [email protected].
More information is online at https://sam.gov/opp/6c4457cee0ca48f09c0331e635711c53/view.
