Passive acoustic detection and localization of drones using MEMS microphones and machine learning

With the rapid proliferation of unmanned aerial vehicles (UAVs) in both civilian and military domains, the demand for efficient detection and tracking systems has become increasingly critical, particularly in sensitive and strategic areas. Conventional surveillance methods, such as radar and infrared sensing, often struggle to detect low-altitude, low-signature UAVs. This study proposes a real-time acoustic localization system based on a distributed array of MEMS microphones. The approach utilizes Time Difference of Arrival (TDOA) estimations to determine the drone’s angular position, combined with a Random Forest classifier to distinguish drone acoustics from environmental noise. A radar-style interface was developed to provide real-time visualization of detections. Field experiments confirmed the system’s effectiveness under diverse environmental conditions. The solution offers a passive, cost-effective alternative for enhancing situational awareness in maritime and other security-sensitive applications.