CUTTING THROUGH THE NOISE
by Nancy Jones-Bonbrest T
he Army Rapid Capabilities Office (RCO) does things differently. It has to. Doing things differently is mandated in its charter and embedded in its culture.
So when it came time for the small acquisition shop to
find a way to speed up signal detection, it knew it wouldn’t seek answers using traditional methods.
Instead, RCO studied commercial models for getting answers quickly and created a “challenge” that gave industry, academia, scientists and other agencies the opportunity to go head-to-head in a competition, with prize money awarded to the top three performers.
Te challenge focused on using artificial intelligence and machine learning to speed up the rate at which electronic warfare officers (EWOs) could sift through the congestion and noise that comes with signal detection. With an ever-increasing number of signals flooding in from satellites, radars, phones and other devices, the signal detection process is no longer efficient in understanding the vast amount of data presented to EWOs on the battlefield.
Within four months of setting up the Army Signal Classification Challenge, RCO knew mathematically who had the best- performing algorithm.
Te challenge also had an unexpected result. By offering an unorthodox method for garnering participation in what would have been a traditional request for information (RFI), the RCO challenge resulted in the top three prize winners spanning the unconventional, including a federally funded research and devel- opment center, an independent group of Australian scientists and a team from a big business.
“By structuring this as a challenge instead of an RFI, we were able to model what industry does and create something much more hands-on,” said Rob Monto, director of RCO’s Emerging Tech- nologies Office. “We invited anyone with a possible capability to participate and posted it on
Challenge.gov and
FBO.gov. Tis is very similar to the commercial model of posting on Kaggle. com, where data sets are sent out to communities of data scien- tists who want to compete against one another to determine who has the best solution.”
RCO’s online challenge offered synthetically generated data based on what could be seen in the electromagnetic spectrum, and chal- lenged participants to prove they had the best artificial intelligence and machine learning algorithm for performing “blind” signal classification quickly and accurately. Te challenge was strictly performance-based and open to anyone. Because it was all online
WINNING TEAM
Team Platypus from The Aerospace Corp. won first prize in the Army Signal Clas- sification Challenge over the summer of 2018. The team includes (front row, from left) Eugene Grayver, Alexander Utter and Andres Vila; and (back row, from left) Donna Branchevsky, Esteban Valles, Darren Semmen, Sebastian Olsen and Kyle Logue. (Photo by Elisa Haber, The Aerospace Corp.)
72
Army AL&T Magazine
January-March 2019
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