Research for the Presence of Unmanned Aerial Vehicle inside Closed Environments with Acoustic Measurements

Small UAVs (unmanned aerial vehicle) can be used in many sectors such as the acquisition of images or the transport of objects. Small UAVs have also been used for terrorist activities or to disturb the flight of airplanes. Due to the small size and the presence of only rotating parts, drones escape traditional controls and therefore represent a danger. This paper reports a methodology for identifying the presence of small UAVs inside a closed environment by measuring the noise emitted during the flight. Acoustic measurements of the noise emitted by a drone inside a large environment (12.0 × 30.0 × 12.0 m) were performed. The noise was measured with a sound level meter placed at different distances (5, 10, and 15 m), to characterize the noise in the absence of anthropic noise. In this configuration, a typical tonal component of drone noise is highlighted at the frequency of one-third of an octave at 5000 Hz due to the rotation of the blades. This component is also present 15 m away from the source point. Subsequent measurements were performed by introducing into the environment, through a loudspeaker, the anthropogenic noise produced by the buzz of people and background music. It is possible to distinguish the typical tonal component of UAV noise at the frequency of 5000 Hz even when the level of recording of anthropogenic noise emitted by the loudspeaker is at the maximum power tested. It is therefore possible to search for the presence of small UAVs inside a specific closed environment with only acoustic measurements, paying attention to the typical frequency of noise emission equal to 5000 Hz.

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

Building Acoustics ◽

10.1177/1351010x20917856 ◽

2020 ◽

pp. 1351010X2091785

Author(s):

Gino Iannace ◽

Giuseppe Ciaburro ◽

Amelia Trematerra

Keyword(s):

Logistic Regression ◽

Unmanned Aerial Vehicle ◽

Indoor Environment ◽

Vehicle Detection ◽

Sound Level ◽

Shopping Center ◽

Model Based ◽

Aerial Vehicle ◽

Level Meter ◽

Indoor Scenarios

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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Educational Implementation of a Sound Level Meter in the LabVIEW Environment

Archives of Acoustics ◽

10.2478/aoa-2013-0003 ◽

2013 ◽

Vol 38 (1) ◽

pp. 19-26 ◽

Cited By ~ 2

Author(s):

Robert Baranski ◽

Grazyna Wszołek

Keyword(s):

Acoustic Signal ◽

Theoretical Background ◽

Sound Level ◽

Practical Training ◽

Acoustic Measurements ◽

Virtual Device ◽

Team Activity ◽

Level Meter ◽

The Moment ◽

Individual Contact

Abstract As a consequence of recent implementations of EU Directives related to noise protection more and more students of various AGH-UST programs are introduced to the basics of acoustic measurements. Students at various levels of theoretical background in the field of acoustic measurements are offered practical training in measurements using digital sound analyzers. The situation would be optimal if each student could have a device at his/her own disposal. Unfortunately, such a situation is not possible at the moment because of various reasons. With the above problem in mind, a dedicated software package has been developed, implemented in the LabVIEW environment, which allows detailed studies of problems related to the acoustic signal measurement using sound level meters, as well as tasks in spectral analysis (1/1 and 1/3 band filters) and narrow-band (FFT) analysis. With such organization during the introductory laboratory classes each student is offered a direct individual contact with a virtual device that is properly pre-programmed for realization of a well-constructed learning process. It definitely facilitates understanding of the essence of acoustic signal measurements and provides a good basis for further laboratory work carried out as a team-activity.

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Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results

E3S Web of Conferences ◽

10.1051/e3sconf/202021104007 ◽

2020 ◽

Vol 211 ◽

pp. 04007

Author(s):

Dede Aulia Rahman ◽

Yudi Setiawan

Keyword(s):

Unmanned Aerial Vehicle ◽

Forest Canopy ◽

Canopy Cover ◽

Thermal Infrared ◽

Sound Level ◽

Flying Height ◽

Infrared Imagery ◽

Aerial Vehicle ◽

Thermal Infrared Imagery ◽

Cover Class

Tropical rainforests are one of the important habitats on earth but are rarely explored because they are diﬃcult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime.

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Model for the prediction of noise generated by fixed sources [Online First]

Ingenieria y Universidad ◽

10.11144/javeriana.iyu23-2.mpng ◽

2019 ◽

Vol 23 (2) ◽

Author(s):

Carlos Alberto Echeverri Londoño ◽

Héctor Jairo Ortiz Pabón

Keyword(s):

Correlation Coefficient ◽

Prediction Method ◽

Weather Conditions ◽

Sound Level ◽

Propagation Model ◽

Experimental Program ◽

Average Error ◽

Noise Emission ◽

Sound Levels ◽

Level Meter

Objective: This article proposes a prediction model applicable to the propagation of noise generated by fixed sources as the result of an analysis of phenomena related to the generation and propagation of sound levels and the subsequent correlation between the levels estimated with the data recorded in the field. Materials and methods: An experimental program was designed that included the measurement of sound pressure levels with a sound level meter to different weather conditions and distances from the noise emission source to compare them with the levels estimated by ISO 9613 Part 2. A statistical analysis of the data recorded in field was performed to observe the dependence with the meteorological variables recorded during the measurements. Results and discussion: Standard error for the proposed prediction method is 11.4 dB, and absolute average error is 9.1 dB. The correlation coefficient of the proposed model is 0.87. A statistically significant relationship exists between the variables with a 95.0% confidence level. Conclusion: A propagation model was obtained that presented a better fit than the method of ISO 9613 Part 2 and a higher correlation coefficient.

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Cooperative Navigation between a Ground Vehicle and an Unmanned Aerial Vehicle in GNSS-Challenged Environments

Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016) ◽

10.33012/2016.14623 ◽

2016 ◽

Author(s):

Victor O. Sivaneri ◽

Jason N. Gross

Keyword(s):

Unmanned Aerial Vehicle ◽

Ground Vehicle ◽

Cooperative Navigation ◽

Aerial Vehicle

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Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

Journal of Applied Reliability ◽

10.33162/jar.2020.12.20.4.332 ◽

2020 ◽

Vol 20 (4) ◽

pp. 332-342

Author(s):

Hyung Jun Park ◽

Seong Hee Cho ◽

Kyung-Hwan Jang ◽

Jin-Woon Seol ◽

Byung-Gi Kwon ◽

...

Keyword(s):

Fault Diagnosis ◽

Unmanned Aerial Vehicle ◽

Sensor Data ◽

Planetary Gearbox ◽

Aerial Vehicle

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Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

Light & Engineering ◽

10.33383/2017-061 ◽

2018 ◽

pp. 7-13

Author(s):

Anton M. Mishchenko ◽

Sergei S. Rachkovsky ◽

Vladimir A. Smolin ◽

Igor V . Yakimenko

Keyword(s):

Spatial Structure ◽

Unmanned Aerial Vehicle ◽

Wavelength Range ◽

Spectral Range ◽

Near Ir ◽

Optoelectronic Systems ◽

Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

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