Presentation Title

**Drone Detection Using Acoustic Harmonics** Exemplary Presentation

Faculty Mentor

Enson Chang

Start Date

18-11-2017 2:30 PM

End Date

18-11-2017 2:45 PM

Location

9-263

Session

Physical Sciences 3

Type of Presentation

Oral Talk

Subject Area

physical_mathematical_sciences

Abstract

Detection of low, slow, and small unmanned aerial systems (LSS-UAS), commonly known as micro or mini drones, continue to be a growing interest to the public and military. This research explores alternative methods for determining the position and features of an LSS-UAS by analyzing its acoustic emission. A simulation was created to test the performance of an Inverse Passive Synthetic Aperture Sonar (IPSAS) processor, which coherently integrated the acoustic emission to gain both signal strength and position resolution. We also collected real acoustic data from a 46 cm civilian-grade drone. The processing procedure included a Low Pass Filter (LPF), subsampling, and a modified range migration algorithm (RMA), accounting for the finite propagation speed of sound. Upon analysis, the acoustic emission collected was less coherent than originally anticipated. Therefore, a Phase Gradient Algorithm (PGA), commonly used in radar processing, was also applied to the data in an effort to correct for random motion-induced signal fluctuations. The result appeared to be a major increase in precision for certain drone frequencies. However, the result was not equally robust over the full drone emission spectrum. We believe that cleaner acoustic data would greatly improve the calculated positional accuracy of IPSAS systems when detecting LSS-UAS.

Summary of research results to be presented

1. A simulation applying a matched-filter was successfully created.

2. Matched filter applied to the acoustic data showed peaks.

3. The applied RMA provided the same results as the matched-filter.

4. PGA along with RMA showed sharper peaks than before.

5. PGA results are not consistent for all frequencies indicate lack of signal coherence.

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Nov 18th, 2:30 PM Nov 18th, 2:45 PM

**Drone Detection Using Acoustic Harmonics** Exemplary Presentation

9-263

Detection of low, slow, and small unmanned aerial systems (LSS-UAS), commonly known as micro or mini drones, continue to be a growing interest to the public and military. This research explores alternative methods for determining the position and features of an LSS-UAS by analyzing its acoustic emission. A simulation was created to test the performance of an Inverse Passive Synthetic Aperture Sonar (IPSAS) processor, which coherently integrated the acoustic emission to gain both signal strength and position resolution. We also collected real acoustic data from a 46 cm civilian-grade drone. The processing procedure included a Low Pass Filter (LPF), subsampling, and a modified range migration algorithm (RMA), accounting for the finite propagation speed of sound. Upon analysis, the acoustic emission collected was less coherent than originally anticipated. Therefore, a Phase Gradient Algorithm (PGA), commonly used in radar processing, was also applied to the data in an effort to correct for random motion-induced signal fluctuations. The result appeared to be a major increase in precision for certain drone frequencies. However, the result was not equally robust over the full drone emission spectrum. We believe that cleaner acoustic data would greatly improve the calculated positional accuracy of IPSAS systems when detecting LSS-UAS.