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Low-cost drone detection system identifies models with 98.5% accuracy at 49 feet

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Low-cost drone detection system identifies models with 98.5% accuracy at 49 feet

A new passive sensing approach has been designed to detect and identify micro-unmanned aerial systems (micro-UAS/drones) near critical infrastructure. 

Every electric drone in the sky carries some sort of signature. It isn’t an encrypted radio ping or a flashing LED light, but an invisible, unavoidable hum: the extremely low-frequency (ELF) magnetic field thrown off by its spinning brushless motors.

Now, researchers at Florida International University (FIU) have figured out how to use ELF to spot incoming threat drones without firing a single signal into the air.

Drones have advanced modern warfare, evolving from high-altitude novelties into cheap, mass-producible airborne weapons. To fight back, the U.S. Department of War requested a $74 billion for drone and counter-drone technologies in its fiscal 2027 budget request. Active radar remains the primary defense against airborne threats, but carries a major vulnerability.

Typical active radar, optical, and acoustic systems face constraints from line-of-sight barriers, environmental background noise, and strict transmission regulations. Passive ELF magnetic sensing reduces these vulnerabilities by covertly capturing motor emissions. This technique operates silently, without broadcasting signals. It offers a stealthy detection alternative unaffected by weather conditions or acoustic interference.

“Every detection method currently in use gives something away. Radar transmits a signal. Acoustic systems get overwhelmed by background noise. We went a different direction entirely,” said Arjuna Madanayake, an Associate Professor at FIU’s College of Engineering & Computing who led the research. 

“While the range of our method is limited for now, it costs only a few hundred dollars to develop and could run continuously without any specialized support, unlike radar systems,” Madanayake said.


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