Acoustic signature measurement of small multi-rotor unmanned aircraft systems

Kloet, Nicola; Watkins, Simon; Clothier, Reece

doi:10.1177/1756829316681868

Cited by 72 publications

(24 citation statements)

References 13 publications

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“…In terms of acoustics, the applications previously mentioned typically put sUAS in close proximity to humans, potentially causing annoyance due to emitted noise. It is shown in [9] that a quadrotor UAS weighing 2.1 Kg can produce an overall sound pressure level A-weighting (OASPLA) between 45 and 65 dBA when 35 to 5 meters away, respectively. According to a psychoacoustic study [10], these overall SPLs would cause the majority of people to be slightly to moderately annoyed.…”

Section: Introductionmentioning

confidence: 99%

Impact of Rotor–Airframe Orientation on the Aerodynamic and Aeroacoustic Characteristics of Small Unmanned Aerial Systems

Wang

Henricks

Zhuang

et al. 2019

Drones

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With the rapid increase in the number of multi-rotor small unmanned aerial systems (sUAS) in recent years and a plethora of possible applications, the aerodynamic and aeroacoustic characteristics of these vehicles become very important issues. Due to the limited research on the aerodynamic and aeroacoustic characteristics of sUAS which include an airframe or support arm, this paper presents a comprehensive analysis of the flow and acoustic features with the inclusion of said geometry. The influence of rotor orientation—either mounted above or below the airframe—was comprehensively studied through experimental and computational analyses. Detailed experimental investigations—including particle image velocimetry (PIV), pressure transducer readings, and acoustic measurements—were employed to assess the aerodynamic and acoustic characteristics of a rotor–airframe system used on typical multirotor sUAS. The results from the computational methodology were also compared to those from the experiment to assess accuracy and possible benefits.

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Section: Introductionmentioning

confidence: 99%

Impact of Rotor–Airframe Orientation on the Aerodynamic and Aeroacoustic Characteristics of Small Unmanned Aerial Systems

Wang

Henricks

Zhuang

et al. 2019

Drones

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“…Using an acoustic camera, Kloet, et al [ 30 ] investigate partial sound sources on a drone. The measurement allows a qualitative assessment of the different components, with the rotors in first place, followed by the motors.…”

Section: Drone Noise Emission Characteristics: Resultsmentioning

confidence: 99%

Drone Noise Emission Characteristics and Noise Effects on Humans—A Systematic Review

Schäffer

Pieren

Heutschi

et al. 2021

IJERPH

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The number of operations of Unmanned Aerial Vehicles (UAV), commonly referred to as “drones”, has strongly increased in the past and is likely to further grow in the future. Therefore, drones are becoming a growing new source of environmental noise pollution, and annoyance reactions to drone noise are likely to occur in an increasing share of the population. To date, research on drone noise emission characteristics, and in particular also on health impacts, seems scarce, but systematic overviews on these topics are missing. The objective of this study was to establish a systematic literature review on drone noise emissions and noise effects on humans. The paper presents the methodology of the systematic reviews performed separately for noise emission and noise effects, assembles current literature, gives an overview on the state of knowledge, and identifies research gaps. Current literature suggests that drone noise is substantially more annoying than road traffic or aircraft noise due to special acoustic characteristics such as pure tones and high-frequency broadband noise. A range of open questions remains to be tackled by future studies.

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“…Finally, an acoustic matrix was used to measure the key frequencies for the main sound sources: motors and propellers. 43…”

Section: Acoustics Signature-based Methodsmentioning

confidence: 99%

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Iannace¹,

Ciaburro²,

Trematerra³

2020

Building Acoustics

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In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor’s vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.

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Acoustic signature measurement of small multi-rotor unmanned aircraft systems

Cited by 72 publications

References 13 publications

Impact of Rotor–Airframe Orientation on the Aerodynamic and Aeroacoustic Characteristics of Small Unmanned Aerial Systems

Impact of Rotor–Airframe Orientation on the Aerodynamic and Aeroacoustic Characteristics of Small Unmanned Aerial Systems

Drone Noise Emission Characteristics and Noise Effects on Humans—A Systematic Review

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

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