Accession Number:

AD1166859

Title:

UAV Payload Identification With Acoustic Emissions and Cell Phone Devices

Descriptive Note:

[Technical Report, Master's Thesis]

Corporate Author:

AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH

Personal Author(s):

• Doster, Hunter G

Report Date:

2022-03-24

Pagination or Media Count:

174

Abstract:

The growing presence of Unmanned Aerial Vehicle UAV brings new threats to the civilian and military front. In response, the Department of Defense DoD is developing many drone detection systems. Current systems use Radio Detection and Ranging RADAR, Light Detection and Ranging LiDAR, and Radio Frequency RF. Although useful, these technologies are becoming easier to spoof every year, and some are limited to line of sight. Acoustic emissions are a unique quality all drones emit. Acoustics are difficult to spoof and do not require line of sight for detection. This research expands the research field of study by creating HurtzHunter, a prototype which tests acoustic payload detection at far range 7 m - 100 m and with cell phone devices. HurtzHunter uses MFCCs to train a SVM for UAV acoustic payload detection. Depending on the recording device and SVM configuration, the results show an 82-98 payload prediction accuracy using cell phone devices.

Descriptors:

Subject Categories:

• Pilotless Aircraft
• Acoustic Detection and Detectors

Distribution Statement:

[A, Approved For Public Release]