“The ‘Drone acoustic detection’ project has shown that small drones can be detected and located very effectively using microphone arrays. The project has led to close cooperation between Monava and CAE Software und Systems GmbH to further develop the technology. The support provided by Aero EDIH was excellent, resulting in very little administrative effort for CAE.”, says Kai-Uwe Kohn, Head of Development, CAE Software und Systems GmbH.
Challenges
Drone services are rapidly expanding, but their ease of use creates critical risks of airspace violations, requiring effective early-warning systems. Traditional active detection sensors often have limitations: they are expensive, require line-of-sight, consume high power, and are subject to regulatory constraints, weather conditions and mobility limitations.
CAE aimed to explore whether passive acoustic detection using MEMS microphone arrays and machine learning could provide reliable situational awareness in nearby airspace. To validate this concept, practical field tests were needed under varying environmental conditions.
Solutions
Aero EDIH engaged Monava AB to conduct indicative tests of acoustic drone detection. A microphone plate equipped with multiple MEMS microphones was used to improve signal-to-noise ratio compared to single microphones.
Several measurements were carried out on drones in Norrköping and Västervik during May and June 2025, under different conditions (windy/calm, noisy/quiet). The acoustic system localized sound sources and distinguished drone types in real time. Recorded data was later used as training material to refine machine learning algorithms and conceptualize findings.
Results and Benefits
Field tests demonstrated that the system can reliably detect micro-sized quadcopters at 200 meters with ±1° accuracy in azimuth and elevation. Fixed-wing drones were detected at 400 meters.
Although some data logging issues occurred, the team believes detection range can be extended further with algorithm optimization. These results reduce uncertainty around acoustic detection and show that a passive, mobile, and cost-effective solution can complement traditional sensors for enhanced airspace security. For CAE, this validates the potential of their acoustic camera technology, paving the way for further investigations of operational deployment.
Perceived social/economic impact
Passive acoustic detection offers a safer, cost-effective way to enhance situational awareness at airports, events, and critical infrastructure – without emitting signals, requiring line-of-sight, or incurring high costs. This strengthens airspace monitoring and creates new business opportunities for CAE in the growing counter-drone market. By detecting drones invisible to radar, the system adds an extra layer of security when combined with other technologies.
Lessons learned
Do’s: As other technologies have limitations and vary in efficiency in relation to weather conditions, the tests should be performed under varied environmental conditions for them to be validated.
Don’ts: Rely solely on acoustic technology. While it effectively addresses gaps where active sensors fall short, it should be integrated into a multi-layered detection system combining several sensor principles for robust performance.
“An innovative system for finding drones with passive sensors, it will be exciting to see the continued development”, says Tomas Westlund, Project Manager, RISE Research Institutes of Sweden.
