ABERDEEN PROVING GROUND, Md. (December 17, 2018) -- After hosting an open online challenge to find the best algorithms to assist Electronic Warfare Officers (EWOs) in sifting through the increasing complexity of signal detection on the battlefield, the Army is now applying that technology to EW systems and operations.The new artificial intelligence (AI) and machine learning (ML) prototypes are being inserted into electronic warfare systems and will be in the hands of select operational units as early as August."When the Army delivered electronic warfare prototypes to Soldiers in Europe earlier this year, we enhanced the sensor footprint for EWOs to track friendly and enemy signals, but we also increased the amount of data they were seeing," said Rob Monto, the director of the Army Rapid Capabilities Office (RCO) Emerging Technologies Office. "That data, which comes from commercial Wi-Fi, cell phones and satellites as well as military systems, is continuously increasing. So we wanted to look at what emerging technologies could be applied to help Soldiers filter to what matters and speed up the operational response."To quickly identify the leading technology available, the Army RCO hosted the Signal Classification Challenge, which concluded in August 2018. The 90-day challenge attracted more than 150 participants and resulted in prize money totaling $150,000 for the top three winners. Ultimately, the Army leveraged a prize-based acquisition approach that yielded AI models capable of efficiently generating AI/ML algorithms trained for a specific problem.Now, the RCO is working on a series of data collection and technical exchange events that will further evaluate the performance and integration of the AI/ML applications for product development and deployment. The various technical approaches discovered through the prior competition are improving the accuracy of current algorithms. Already, the team has integrated an AI framework into the sensor processing hardware for the Army's Tactical Electronic Warfare System (TEWS), with the concept that this framework can be scaled to additional Army platforms and systems.Next, the RCO will work with select vendors and EWOs during an operational evaluation that includes data generation, collection and algorithm testing at Yuma Proving Ground, Ariz. The event, planned for early-to-mid 2019, will lead to fielding the new technology to an operational unit later in the year."This is an example of harnessing AI and ML to meet a very specific need," Monto said. "The top submissions showed very novel approaches to utilizing today's emerging tech to accurately classify signals, and we decided to move forward in getting them to the field for feedback from the Soldiers who use the equipment."While the EWOs will remain as the lead for identifying signals of interest and analyzing their impact, the use of AI and ML could help them quickly and accurately detect patterns, identify signals of interest, filter out unwanted signal noise and paint a picture of the electromagnetic spectrum, providing a form of "AI overwatch." Along with automating aspects of the signal classification process using AI, another objective of the project is to promote reusability of the technology across the services by developing AI algorithm training pipelines and putting in place sustainment capabilities specific to the EW domain.The idea for the Signal Classification Challenge stemmed from the Army's delivery of its first electronic warfare systems for brigade and below in response to an operational needs statement from U.S. Army Europe. The RCO rapidly prototyped and delivered the systems in partnership with the Project Manager for Electronic Warfare & Cyber.The RCO announced challenge winners on Aug. 27, 2018, with first place going to Team Platypus from The Aerospace Corp., a national nonprofit corporation that operates a federally funded research and development center; second place to Team AU, a small team of independent Australian data scientists; and third place to THUNDERINGPANDA of Motorola Solutions.The RCO is working with the winners and their approaches in operationalizing the technology.