Aerospace wins U.S. Army contest to bring AI capabilities to soldiers

A team from Aerospace Corporation won a U.S. Army challenge designed to identify artificial intelligence and machine learning tools that could improve the speed and accuracy of electronic warfare operations.

The Army Signal Classification Challenge invited participants to prove they had the best artificial intelligence and machine learning algorithms for performing “blind” radio frequency signal classification quickly and accurately.

An Interim Armored Vehicle “Stryker” and AH-64 Apache helicopters with Battle Group Poland move to secure an area during a lethality demonstration as part of Saber Strike 18 in June 2018. (Photo: U.S. Army/Spc. Hubert D. Delany III, 22nd Mobile Public Affairs Detachment)

The goal was to find solutions that could reduce the cognitive burden placed on electronic warfare soldiers by identifying signals of interest in the electromagnetic spectrum.

The Army , Rapid Capabilities Office (RCO) launched the challenge because the classic signal detection process is no longer efficient in understanding the vast amount of information presented to electronic warfare soldiers on the battlefield by an ever-increasing number of satellite signals, radars, phones and other devices.

More than 150 teams from across universities, laboratories, industry and government participated. The first-place award of $100,000 went to Platypus Aerospace from Aerospace Corporation, a federally funded research and development center.

Second place, with an award of $30,000, went to TeamAU, made up of a team of individual Australian data scientists. Third place and $20,000 went to THUNDERINGPANDA of Motorola Solutions.

“The amount of interest and quality of performance was remarkable, including from nontraditional organizations,” said Rob Monto, Emerging Technologies director for the RCO. “In doing this as a challenge, instead of a traditional Request for Information, we were really modeling what industry does to get at a problem quickly. It was performance-based, open to anyone and implemented without a lot of cost or burden placed on those entering. And now, in a matter of less than four months, we know mathematically who has the best performance for this initial step of applying AI and machine learning to signal classification.”

The challenge, which opened on April 30 and closed on Aug. 13, gave participants 90 days to develop their models and work with training datasets provided by the RCO. That was followed by two test datasets of varying complexity that were the basis for judging submissions.

Participants’ overall challenge score was based on a combined weighted score for both test datasets. Participants were also able to see how they were performing in relation to others in real time, via the participant leaderboard.

“This challenge targeted the upfront data collection, which is traditionally very labor intensive and time consuming,” Monto said. “Now we have a very accurate, very rapid algorithm for a specific problem set. With this research done on the front end, we can move forward with trying to build and integrate it into a real solution for the Army.”

A second phase of the competition is planned and details will be announced later this year.

“We’re thrilled to see our team win this competition through their novel application of artificial intelligence to secure the use and protection of the radio frequency spectrum,” said Steve Isakowitz, Aerospace president and CEO. “Their accomplishment is another great example of how Aerospace is employing cutting-edge technology to advance next-generation capabilities for the warfighter while solving one of our customer’s most difficult challenges.”

Aerospace engineers, named “Team Platypus,” win the Army AI Challenge. From left: Eugene Grayver, Alexander Utter, Andres Vila, Donna Branchevsky, Esteban Valles, Darren Semmen, Sebastian Olsen, Kyle Logue (not pictured). (Photo: Aerospace Corp.)

The group, known as “Team Playtpus,” consists of eight Aerospace communications systems and artificial intelligence engineers: Andres Vila, Kyle Logue, Esteban Valles, Donna Branchevsky, Sebastian Olsen, Alexander Utter, Darren Semmen and Eugene Grayver.

Out of more than 150 overall participants, including 49 teams that actively competed in the challenge, the Aerospace team won by correctly detecting and classifying the greatest number of radio frequency signals using a combination of signal processing and AI technologies.

“In its challenge, the Army RCO released a training set with synthesized data that the teams used to build their algorithms,” said Andres Vila, Aerospace team lead. “Our goal was to combine the team’s deep history and expertise in advanced satellite communications with our practical knowledge of the latest in machine learning and deep neural networks to provide a best-in-class solution.”

Vila added, “This win means that we have built a team that can excel in this new and exciting field of machine learning and specifically deep learning solutions for communication problems.”