Improvements to the Stevens drone acoustic detection system

Daniel Kadyrov, Alexander Sedunov, Nikolay Sedunov, Alexander Sutin, Hady Salloum, Sergey Tsyuryupa

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Stevens Institute of Technology (SIT) designed and built multiple acoustic sensors to detect and track drones using Steered-Response Phase Transform (SRP-PHAT) and classify them using narrow-band frequencies. SIT improved a previously built four-microphone system by increasing the number of microphones to seven, modifying the software, and improving the testing algorithm for system performance estimation. The four- and the seven-microphone systems were deployed during several tests conducted with multi-rotor sUAS of different sizes, including the DJI Inspire 2, DJI Mavic 2 Pro, Autel Robotics EVO II Pro, and the Intel Falcon 8 performing flight patterns at various distances, elevations, and speeds. Acoustic signatures were collected and detection distances were compared for the tested drones. Measurements from the field test were used to recalculate UAS acoustic signatures to 1 meter. These signatures were applied to develop a simple method of acoustic detection distance estimation using the passive sonar equation. The improved seven-microphone system provided farther detection distances than the four-microphone system.

Original languageEnglish
Article number45001
JournalProceedings of Meetings on Acoustics
Volume46
Issue number1
DOIs
StatePublished - 23 May 2022
Event182nd Meeting of the Acoustical Society of America, ASA 2022 - Denver, United States
Duration: 23 May 202227 May 2022

Fingerprint

Dive into the research topics of 'Improvements to the Stevens drone acoustic detection system'. Together they form a unique fingerprint.

Cite this