As the number of small drones on the battlefield grows, so does demand for affordable countermeasures. In its fiscal year 2027 budget request, the Pentagon asked for 74 billion dollars for drone and counter-drone technologies. Against this backdrop, researchers at Florida International University (FIU) have proposed a passive detection method: they pick up the ultra-low-frequency magnetic fields created by drones' spinning electric motors. The work was published in IEEE Sensors Journal.
Different drone models have their own motor configuration, so each leaves a unique low-frequency magnetic "fingerprint." The team recorded these signals with a special antenna and processed the data with a machine learning model - a program that learns to find patterns from examples. In outdoor tests, the system identified a drone with accuracy of up to 98.52% at a distance of up to 15 meters (49 feet), and not only the fact of its presence but also the specific make and model.
Unlike standard systems, the method does not transmit an active signal. "Any method used today gives something away: radar emits a signal, acoustic systems suffer from background noise. We went in a completely different direction," FIU professor Arjuna Madanayake was quoted as saying in a press release. According to him, the range is still limited, but the device costs only a few hundred dollars and can operate continuously without special maintenance, unlike radars.
The study focuses on micro-UAVs - small, mass-produced drones capable of carrying explosives or conducting reconnaissance while being almost invisible to conventional radars. The FIU device passively receives low-frequency radiation without sending a return signal, so the enemy has nothing to track.
"Knowing exactly which drone is approaching matters: different craft carry different payloads and pose different threats. The more information a defender has, the better they can respond," Madanayake noted. The group is now working with U.S. industry on further developing the technology.
The team led by Madanayake from FIU's Department of Electrical and Computer Engineering also includes Sri Lankan researcher Chatura Seneviratne and Sumyajit Mandal, a staff member at Brookhaven National Laboratory.