Drone Detection and Localization Using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology

• Published in: Journal of lightwave technology Vol. 41; no. 3; pp. 822 - 831
• Main Authors: Fang, Jian, Li, Yaowen, Ji, Philip N., Wang, Ting
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic array processing; Acoustic mapping; Acoustic sensors; Acoustics; Airborne sensing; Arrays; Data integration; Data management; distributed acoustic sensing; drone detection; Drones; Fiber optics; fiber-optic acoustic sensor; Field tests; Localization; Location awareness; Microphones; Optical fiber sensors; Optical fibers; Optics; Remote sensing; Root-mean-square errors; Sensitivity; Signal processing; Sound; Sound fields; Synchronism; unmanned aerial vehicle; Vibration monitoring; Waveforms
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2022.3208451

In recent years, the widespread use of drones has led to serious concerns about safety and privacy. Drone detection using microphone arrays has proven to be a promising method. However, it is challenging for microphones to serve large-scale applications due to the issues of synchronization, complexity, and data management. Moreover, distributed acoustic sensing (DAS) using optical fibers has demonstrated its advantages in monitoring vibrations over long distances but does not have the necessary sensitivity for weak airborne acoustics. In this work, we present, to the best of our knowledge, the first fiber-optic quasi-distributed acoustic sensing demonstration for drone surveillance. We develop enhanced fiber-optic acoustic sensors (FOASs) for DAS to detect drone sound. The FOAS shows an ultra-high measured sensitivity of −101.21 re. 1rad/μPa, as well as the capability for high-fidelity speech recovery. A single DAS can interrogate a series of FOASs over a long distance via optical fiber, enabling intrinsic synchronization and centralized signal processing. We demonstrate the field test of drone detection and localization by concatenating four FOASs as a sensing array and capturing the airborne sound remotely through DAS. Both the waveforms and spectral features of the drone sound are recognized. With acoustic field mapping and data fusion, accurate drone localization is achieved with a root-mean-square error (RMSE) of 1.47 degrees. This approach holds great potential in large-scale sound detection applications, such as drone detection or city event monitoring.