TY - GEN
T1 - UAV Passive Acoustic Detection
AU - Sedunov, Alexander
AU - Salloum, Hady
AU - Sutin, Alexander
AU - Sedunov, Nikolay
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/12
Y1 - 2018/12/12
N2 - The proliferation of low-cost consumer Unmanned Aerial Vehicles (UAV) has enabled their potential nefarious use or negligent misuse, including intrusion into airspace used by emergency services or civilian aircraft, unauthorized surveillance, and delivery of harmful payloads. Passive acoustic sensors may permit the creation of low-cost means of detecting and localizing the unwanted UAV traffic. Experiments were conducted to characterize the emitted noise of UAVs of various sizes in an anechoic chamber while airborne and demonstrate the processing required to detect and find the direction toward the sound. An array of microphones arranged in two circular tiers, each with a radius of 1 meter, separated by 1.6 meters vertically was used for data collection in the tests at a local airport. Algorithms based on Generalized Cross-Correlation (GCC) were applied for direction finding including fusing time difference of arrival and steered power response with phase transform (SRP-PHAT). Detection distances of 294 m for the smallest UAS tested were demonstrated. An algorithm for tracking moving sources using microphones separated by about 19 meters was demonstrated, addressing the decorrelation due to the Differential Doppler effect.
AB - The proliferation of low-cost consumer Unmanned Aerial Vehicles (UAV) has enabled their potential nefarious use or negligent misuse, including intrusion into airspace used by emergency services or civilian aircraft, unauthorized surveillance, and delivery of harmful payloads. Passive acoustic sensors may permit the creation of low-cost means of detecting and localizing the unwanted UAV traffic. Experiments were conducted to characterize the emitted noise of UAVs of various sizes in an anechoic chamber while airborne and demonstrate the processing required to detect and find the direction toward the sound. An array of microphones arranged in two circular tiers, each with a radius of 1 meter, separated by 1.6 meters vertically was used for data collection in the tests at a local airport. Algorithms based on Generalized Cross-Correlation (GCC) were applied for direction finding including fusing time difference of arrival and steered power response with phase transform (SRP-PHAT). Detection distances of 294 m for the smallest UAS tested were demonstrated. An algorithm for tracking moving sources using microphones separated by about 19 meters was demonstrated, addressing the decorrelation due to the Differential Doppler effect.
KW - Passive acoustics
KW - Signal processing
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85060443805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060443805&partnerID=8YFLogxK
U2 - 10.1109/THS.2018.8574129
DO - 10.1109/THS.2018.8574129
M3 - Conference contribution
AN - SCOPUS:85060443805
T3 - 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018
BT - 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018
T2 - 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018
Y2 - 23 October 2018 through 24 October 2018
ER -