U.S. military researchers needed new ways of using computer algorithms and high-performance embedded computing (HPEC) for radar target recognition to identify military targets rapidly and accurately using radar sensors on manned and unmanned tactical aircraft. They found their solution from Deep Learning Analytics LLC in Arlington Va.
Officials of the U.S. Air Force Research Laboratory at Wright-Patterson Air Force Base, Ohio, announced a $6 million contract this week to Deep Learning Analytics for the Target Recognition and Adaption in Contested Environments (TRACE) program. The Air Force awarded the contract on behalf of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va.
The DARPA TRACE program has three goals: military target recognition on low-power aircraft; low false-alarm rates for targets deployed in complex environments; and rapid learning of new targets with sparse or limited measured training data.
In a target-dense environment, the adversary has the advantage of using sophisticated decoys and background traffic to degrade the effectiveness of existing automatic target recognition (ATR) solutions, DARPA researchers explain.