Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Djurek, Ivan; Petošić, Antonio; Grubeša, Sanja; Suhanek, Mia

doi:10.1109/access.2020.2965177

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…Considering the sound emitted by a generic drone, several components contribute to defining the pitch of the emitted sound. In line with related works on sound analysis, we found that two components are mainly contributing to the sound: motors and blades [40].…”

Section: Sound Components Analysissupporting

confidence: 91%

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Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Ibrahim¹,

Sciancalepore²,

Pietro³

2020

Preprint

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The increasing popularity of autonomous and remotely-piloted drones have paved the way for several use-cases, e.g., merchandise delivery and surveillance. In many scenarios, estimating with zero-touch the weight of the payload carried by a drone before its physical approach could be attractive, e.g., to provide an early tampering detection.In this paper, we investigate the possibility to remotely detect the weight of the payload carried by a commercial drone by analyzing its acoustic fingerprint. We characterize the difference in the thrust needed by the drone to carry different payloads, resulting in significant variations of the related acoustic fingerprint. We applied the above findings to different usecases, characterized by different computational capabilities of the detection system. Results are striking: using the Mel-Frequency Cepstral Coefficients (MFCC) components of the audio signal and different Support Vector Machine (SVM) classifiers, we achieved a minimum classification accuracy of 98% in the detection of the specific payload class carried by the drone, using an acquisition time of 0.25 s-performances improve when using longer time acquisitions.All the data used for our analysis have been released as opensource, to enable the community to validate our findings and use such data as a ready-to-use basis for further investigations.

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Section: Sound Components Analysissupporting

confidence: 91%

“…We can see that the strongest components, i.e., the frequency components characterized by higher sound emissions, are located in the low-frequency range, below 7 kHz. In line with the findings of the authors in [40], we identify these frequency components to be associated with the blades of the drone.…”

Section: Sound Components Analysissupporting

confidence: 89%

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Ibrahim¹,

Sciancalepore²,

Pietro³

2020

Preprint

View full text Add to dashboard Cite

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“…In order to improve the signal-to-noise ratio (SNR), it is possible to take into account the acoustic signature of the UAVs. Indeed, many studies have shown that multi-rotor drones and Radio Controlled airplanes produce harmonic structured acoustic signals [ 16 , 17 , 18 ]. From this observation, the signal recorded by the microphone can be filtered by taking into account the fundamental frequency of the UAV signal.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane

Itare

Thomas

Raoof

et al. 2022

Sensors

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The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV’s acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer’s output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.

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“…Depending on the mechanism of occurrence, a significant factor that affects the tonal or broadband frequency band is related to the blade passing frequency [9,10]. A typical drone has more than four rotors, which creates a more complex noise field environment [11,12]. Furthermore, the operating noise of a drone in close proximity to a microphone can cause the sound to be masked and indistinguishable.…”

Section: Introductionmentioning

confidence: 99%

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Choi

2021

Drones

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Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

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Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Abstract: This work was supported in part by the European Union from the European Regional Development Fund (ERDF) under Project KK.01.2.1.01.0103 4D Acoustical Camera (in Croatian: 4D Akustička kamera).

Cited by 19 publications

References 16 publications

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

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