Advances in Underwater Acoustics and Aeroacoustics

Abstract submission deadline

28 December 2024

Manuscript submission deadline

31 March 2025

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Image courtesy of Dr. Claudio Testa

Topic Information

Dear Colleagues,

It is our great pleasure to announce a new Topic titled “Advances in Underwater Acoustics and Aeroacoustics”. Noise pollution impacts people and mammals, and the increased use of high-performance engines for ships, aircrafts, helicopters, and drones has limited acoustic certification by the shipbuilding and air-vehicle manufacturing industries. In order to share your own experiences and challenges on new theoretical–numerical methodologies and experimental techniques in the field of noise prediction, measurement, and control, the present Topic aims to bring together first-class articles on experiments and the modelling of sound generation and propagation mechanisms in free-space or in the presence of scattering phenomena for marine and aeronautical configurations. This Topic is expected to attract widespread attention and will have an excellent impact on the field of aero/hydro-dynamically generated sound. It will include (but is not limited to) the following:

• Propeller noise;
• Rotor noise;
• Jet noise;
• Acoustic scattering;
• Far-field sound propagation;
• Sound source identification and location;
• Sound wave reconstruction;
• Noise mitigation;
• Noise cancellation;
• Noise signal decoupling.

Dr. Claudio Testa
Prof. Dr. Giovanni Bernardini
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Acoustics acoustics	1.3	3.7	2019	19.4 Days	CHF 1600	Submit
Aerospace aerospace	2.1	3.4	2014	24 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Vehicles vehicles	2.4	4.1	2019	24.7 Days	CHF 1600	Submit
Modelling modelling	1.3	2.7	2020	21.2 Days	CHF 1000	Submit

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Published Papers (5 papers)

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All Acoustics Aerospace Applied Sciences JMSE Remote Sensing Vehicles Modelling

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24 pages, 42566 KiB  

Open AccessArticle

Deblurring of Beamformed Images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution

by Zijie Zha, ** Yan, **aobin **, Shilong Wang and Delin Wang

Remote Sens. 2024, 16(13), 2411; https://doi.org/10.3390/rs16132411 - 1 Jul 2024

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Abstract

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the [...] Read more.

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS). Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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21 pages, 5488 KiB  

Open AccessArticle

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

by Chenglei Lv, Qiushi Sun, Huifang Chen and Lei **e

J. Mar. Sci. Eng. 2024, 12(2), 338; https://doi.org/10.3390/jmse12020338 - 16 Feb 2024

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Abstract

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear [...] Read more.

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity. Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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18 pages, 2890 KiB  

Open AccessArticle

The Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Boundary Layer Wall Pressure in Aircraft Using Machine Learning Regression Techniques

by Zachary Huffman and Joana Rocha

Aerospace 2024, 11(6), 446; https://doi.org/10.3390/aerospace11060446 - 31 May 2024

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Abstract

Aircraft cabin noise poses a health risk for regular passengers and crew, being connected to a heightened risk of cardiovascular disease, hearing loss, and sleep deprivation. At cruise conditions, its most significant cause is random pressure fluctuations in the turbulent boundary layer of [...] Read more.

Aircraft cabin noise poses a health risk for regular passengers and crew, being connected to a heightened risk of cardiovascular disease, hearing loss, and sleep deprivation. At cruise conditions, its most significant cause is random pressure fluctuations in the turbulent boundary layer of aircraft, and as such the derivation of an accurate model to predict the power spectral density of these fluctuations remains an important ongoing research topic. Early models (such as those by Lowson and Robertson) were derived by simplifying the governing equations, the Reynolds-averaged Navier Stokes equations, and solving for fluctuating pressure. Most subsequent equations were derived either by applying statistical and mathematical techniques to simplify the Robertson and Lowson models or by making modifications to address apparent shortcomings. Overall, these models have had varying success—most are accurate near the Mach and Reynolds numbers they were designed for, but less accurate under other conditions. In response to this shortcoming, Dominique demonstrated that a novel technique (machine learning, specifically artificial neural networking) could produce a model that is accurate under most flight conditions. This paper extends this research further by applying a different machine learning technique (nonlinear least squares regression analysis) and dimensional analysis to produce a new model. The resulting equation proved accurate under its design conditions of low airspeed (approximately 11 m/s) and low turbulent Reynolds number (approximately 850,000). However, a larger dataset with more diverse flight conditions would be required to make the model more generally applicable. Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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19 pages, 3232 KiB  

Open AccessArticle

Harmonic Source Depth Estimation by a Single Hydrophone under Unknown Seabed Geoacoustic Property

by **aolei Li, Yang** Xu, Wei Gao, Haozhong Wang and Liang Wang

Remote Sens. 2024, 16(12), 2227; https://doi.org/10.3390/rs16122227 - 19 Jun 2024

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Abstract

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound [...] Read more.

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound source depth estimation algorithm, seabed independent depth estimation (SIDE) algorithm, is proposed. This algorithm combines the estimated mode depth functions, modal amplitudes, and the sign of each modal to estimate the sound source depth. The performance of the SIDE algorithm is analyzed by simulations. Results show that the SIDE is insensitive to the initial range of the sound source, the source depth, the hydrophone depth, the source velocity, and the type of the seabed. Finally, the effectiveness of the SIDE algorithm is verified by the SWellEX-96 data. Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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22 pages, 5972 KiB  

Open AccessArticle

Maximum Likelihood Deconvolution of Beamforming Images with Signal-Dependent Speckle Fluctuations

by Yuchen Zheng, **aobin **, Lingxuan Li and Delin Wang

Remote Sens. 2024, 16(9), 1506; https://doi.org/10.3390/rs16091506 - 24 Apr 2024

Viewed by 564

Abstract

Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple [...] Read more.

Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays. Full article

(This article belongs to the Topic Advances in Underwater Acoustics and Aeroacoustics)

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