Defense Advanced Research Projects AgencyTagged Content List

Air Systems

Manned and unmanned aerial systems, including fixed-wing and rotary-wing aircraft and supporting technologies

Showing 13 results for Air + Algorithms RSS
August 18-20,
Webinar
The third and final competition in DARPA’s AlphaDogfight Trials will take place virtually August 18-20 instead of in person due to the ongoing COVID-19 pandemic. Participating teams and audience members will watch online as artificial intelligence (AI) algorithms control simulated F-16 fighters in aerial combat, culminating in a matchup on August 20 between the top AI and an experienced Air Force fighter pilot flying a virtual reality F-16 simulator.
The ACE program seeks to increase trust in combat autonomy by using human-machine collaborative dogfighting as its challenge problem. This also serves as an entry point into complex human-machine collaboration. ACE will apply existing artificial intelligence technologies to the dogfight problem in experiments of increasing realism. In parallel, ACE will implement methods to measure, calibrate, increase, and predict human trust in combat autonomy performance.
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. Airborne strike operations against relocatable targets require that pilots fly close enough to obtain confirmatory visual identification before weapon release, putting the manned platform at extreme risk. Radar provides a means for imaging ground targets at safer and far greater standoff distances; but the false-alarm rate of both human and machine-based radar image recognition is unacceptably high. Existing ATR algorithms also require impractically large computing resources for airborne applications.