Advances in Underwater Acoustics and Aeroacoustics

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Acoustics acoustics	1.3	3.7	2019	20 Days	CHF 1600
Aerospace aerospace	2.1	3.4	2014	21.3 Days	CHF 2400
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Vehicles vehicles	2.4	4.1	2019	19.9 Days	CHF 1600
Modelling modelling	1.3	2.7	2020	18.9 Days	CHF 1000

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 Xie

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

Cited by 1 | Viewed by 1793

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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22 pages, 5972 KiB

Open AccessArticle

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

by Yuchen Zheng, Xiaobin Ping, Lingxuan Li and Delin Wang

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

Cited by 3 | Viewed by 1283

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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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

Viewed by 998

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 Xiaolei Li, Yangjin Xu, Wei Gao, Haozhong Wang and Liang Wang

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

Viewed by 946

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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24 pages, 42566 KiB

Open AccessArticle

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

by Zijie Zha, Xi Yan, Xiaobin Ping, Shilong Wang and Delin Wang

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

Cited by 1 | Viewed by 1304

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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19 pages, 4428 KiB

Open AccessArticle

The Depth Distribution Law of the Polarization of the Vector Acoustic Field in the Ocean Waveguide

by Yizheng Wei and Chao Sun

J. Mar. Sci. Eng. 2024, 12(8), 1325; https://doi.org/10.3390/jmse12081325 - 5 Aug 2024

Cited by 1 | Viewed by 1018

Abstract

The polarization of the acoustic field in the ocean waveguide environment is a unique property that can provide new ideas for locating and detecting the underwater target, so it is interesting to study the polarization. This paper extends the Stokes parameters to a [...] Read more.

The polarization of the acoustic field in the ocean waveguide environment is a unique property that can provide new ideas for locating and detecting the underwater target, so it is interesting to study the polarization. This paper extends the Stokes parameters to a broadband form, and uses the non-stationary phase approximation method to simplify the expressions, reducing the complexity of theoretical derivation. A physical phenomenon is observed where polarization exhibits significant variations concerning the sea surface, seafloor, source depth, and the source symmetrical depth. Simulation results demonstrate that the simplified equations using the non-stationary phase approximation are effective. Additionally, by normalizing the broadband Stokes parameters, the effects of horizontal range on the depth distribution law of polarization can be eliminated. Subsequently, using the normalized broadband Stokes parameters, the influence of environmental and source parameters on the depth distribution law of polarization is analyzed. The effectiveness of the non-stationary phase approximation and the range-independence property of the normalized broadband Stokes parameters are verified by processing RHUM-RUM experimental data. Based on the conclusions of this paper, it is expected that the polarization can be used for target depth estimation. Full article

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

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12 pages, 5095 KiB

Open AccessArticle

Seafloor Topographic Data Processing in Near-Seafloor Acoustic Field Simulation

by Siyu Ma, Yuxiang Zhang, Zhinan Xie and Jun Li

Acoustics 2024, 6(4), 966-977; https://doi.org/10.3390/acoustics6040053 - 31 Oct 2024

Viewed by 1360

Abstract

Near-seafloor acoustic field characteristics are essential prior knowledge for near-seafloor underwater acoustic engineering. Scholte waves are a crucial component of the near-seafloor acoustic fields. These fields, when considering Scholte waves, are susceptible to seafloor relief. However, open-source bathymetric datasets generally lack sufficient precision. [...] Read more.

Near-seafloor acoustic field characteristics are essential prior knowledge for near-seafloor underwater acoustic engineering. Scholte waves are a crucial component of the near-seafloor acoustic fields. These fields, when considering Scholte waves, are susceptible to seafloor relief. However, open-source bathymetric datasets generally lack sufficient precision. Therefore, acoustic field simulations using open-source data can contain significant errors or even introduce erroneous propagation characteristics. The spectral element method (SEM) is an example of exploring the influence of an inadequate spatial-sampling rate and sea-depth precision on acoustic field simulations. In this article, appropriate methods for topographic processing are presented. The results indicate that the terrain can be corrected using cubic spline interpolation in cases of an inadequate spatial-sampling rate. Where there is insufficient sea-depth precision, this study proposes a terrain processing method. The first step involves sequentially determining the interpolation points for the rising and falling edge, depressions, bulges, and horizontal segments. Then, it adopts cubic spline interpolation. The SEM examples effectively verify this effect. Given the limited research on terrain correction in acoustic field simulations, this study introduces a low-complexity method that can effectively support exploring acoustic fields affected by seafloor terrain. Full article

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

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24 pages, 2016 KiB

Open AccessArticle

Acoustic Transmission Loss of a Cylindrical Silencer Filled with Multilayer Poroelastic Materials Based on Mode-Matching Method

by Haesang Yang and Woojae Seong

J. Mar. Sci. Eng. 2024, 12(11), 2109; https://doi.org/10.3390/jmse12112109 - 20 Nov 2024

Viewed by 777

Abstract

The efficacy of silencers in reducing piping noise is contingent upon the specific installation and operating environment. Among the various forms of silencers, the acoustic characteristics of dissipative silencers with sound-absorbing materials attached internally exist in an area that is difficult to explain [...] Read more.

The efficacy of silencers in reducing piping noise is contingent upon the specific installation and operating environment. Among the various forms of silencers, the acoustic characteristics of dissipative silencers with sound-absorbing materials attached internally exist in an area that is difficult to explain by existing theories. This is dependent upon the specific type and placement of the attached sound-absorbing materials. This paper presents a methodology for calculating the acoustic transmission loss (TL) of a cylindrical silencer filled with a multilayer poroelastic material, employing the mode-matching method. To describe the numerical process of treating waves propagating within a poroelastic material and determine the modes in accordance with the boundary conditions necessary for analyzing the acoustic performance of the silencer, the Biot model and the Johnson–Champoux–Allard–Lafarge model were employed. The obtained modes were utilized to calculate the acoustic TL of silencers filled with single, double, and triple layers of poroelastic materials. In particular, the results obtained for the single layer were validated by comparing them with the results of a finite element analysis, and the results obtained for multiple layers with the same material were validated by comparing them with the equivalent single-layer results. Moreover, the results of the numerical calculations of the acoustic TLs of the silencer for three distinct types of poroelastic materials, including those with varying degrees of frame rigidity or softness, were compared, and the acoustic characteristics were analyzed in relation to the intrinsic properties of the materials and their arrangement. It is anticipated that the methodology presented in this paper will facilitate the design of silencers using poroelastic materials in accordance with the specific requirements of users or designers by allowing for a comprehensive consideration of the thickness of layers and the arrangement of materials. Full article

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

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20 pages, 4829 KiB

Open AccessArticle

Study on Sound Field Properties of Parametric Array Under the Influence of Underwater Waveguide Interface Scattering Based on Non-Paraxial Model—Theory and Experiment

by Yuan Cao, Jie Shi, Jiangyi Zhang, Yuezhu Cheng and Haokang Shi

J. Mar. Sci. Eng. 2025, 13(2), 286; https://doi.org/10.3390/jmse13020286 - 4 Feb 2025

Viewed by 616

Abstract

This paper theoretically and experimentally studies the effect of underwater waveguide interface scattering on the nonlinear sound field characteristics of parametric array (PA) radiation. Based on the image source method, the components of the sound field in the waveguide are first analyzed. Then, [...] Read more.

This paper theoretically and experimentally studies the effect of underwater waveguide interface scattering on the nonlinear sound field characteristics of parametric array (PA) radiation. Based on the image source method, the components of the sound field in the waveguide are first analyzed. Then, a non-paraxial model is developed to account for the influence of interface scattering. This model enables accurate calculation of the wide-angle sound field. The impact of the sound source depth and the interface reflection coefficient on the distribution of the difference-frequency wave (DFW) sound field in the waveguide is studied. The interface alters the phase distribution of the DFW’s virtual source density function, thereby affecting the sound field accumulation process. Waveguide interfaces with different absorption coefficients influence the amplitude oscillation caused by interface reflection and change the sidelobe size of the DFW beam. The DFW sound field distribution is measured at three typical frequencies. Simulation and experimental results show that the attenuation of the DFW’s axial sound pressure level in the waveguide oscillates, and the DFW’s beamwidth gradually widens as the frequency decreases. The calculated results from the proposed model agree well with the measured data, with average errors along the sound axis and depth being less than 3 dB and 6 dB, respectively. This demonstrates the model’s superior applicability compared to the existing free-field model. Full article

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

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17 pages, 7596 KiB

Open AccessArticle

Phase Estimation Using an Optimization Algorithm to Improve Ray-Based Blind Deconvolution Performance

by Wonjun Yang and Dong-Gyun Han

J. Mar. Sci. Eng. 2025, 13(4), 704; https://doi.org/10.3390/jmse13040704 - 1 Apr 2025

Viewed by 282

Abstract

Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied [...] Read more.

Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied in underwater acoustics. However, the accuracy of CIR estimation in RBD may be compromised by phase uncertainty in the source waveform, necessitating enhancements in its performance. This paper proposes a method to improve RBD performance by estimating the phase of the source waveform using an optimization algorithm. Specifically, the particle swarm optimization (PSO) algorithm is employed to minimize phase estimation errors by optimizing the time delay for each receiver to maximize the beamformer output. The effectiveness of the proposed method was evaluated using two types of source signals: ship noise and linear frequency modulation (LFM), which corresponded to relatively low- and high-frequency sources, respectively. Performance comparisons with conventional RBD across various source-to-vertical line array distances revealed that the proposed method yielded more compact arrival paths with reduced time spread and a higher signal-to-noise ratio at short distances in the low-frequency band, and it consistently outperformed conventional RBD at all distances in the high-frequency band. Full article

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

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23 pages, 8225 KiB

Open AccessArticle

Parallel Net: Frequency-Decoupled Neural Network for DOA Estimation in Underwater Acoustic Detection

by Zhikai Yang, Xinyu Zhang, Zailei Luo, Tongsheng Shen, Mengda Cui and Xionghui Li

J. Mar. Sci. Eng. 2025, 13(4), 724; https://doi.org/10.3390/jmse13040724 - 4 Apr 2025

Viewed by 334

Abstract

Under wideband interference conditions, traditional neural networks often suffer from low accuracy in single-frequency direction-of-arrival (DOA) estimation and face challenges in detecting single-frequency sound sources. To address this limitation, we propose a novel model called Parallel Net. The architecture adopts a frequency-parallel [...] Read more.

Under wideband interference conditions, traditional neural networks often suffer from low accuracy in single-frequency direction-of-arrival (DOA) estimation and face challenges in detecting single-frequency sound sources. To address this limitation, we propose a novel model called Parallel Net. The architecture adopts a frequency-parallel design: it first employs a recurrent neural network, the generalized feedback gated recurrent unit (GFGRU), to independently extract features from each frequency component, and then it fuses these features through an attention mechanism. This design significantly enhances the network’s capability in estimating the DOA of single-frequency signals. The simulation results demonstrate that when the signal-to-noise ratio (SNR) exceeds −10 dB, Parallel Net achieves a mean absolute error (MAE) below 2°, outperforming traditional frequency-coherent neural networks and the MUSIC algorithm, and reduces the error to half that of classical beamforming (CBF). Further validation on the SWellEx-96 experiment confirms the model’s effectiveness in detecting single-frequency sources under wideband interference. Parallel Net exhibits superior sidelobe suppression and fewer spurious peaks compared to CBF, achieves higher accuracy than MUSIC, and produces smoother and more continuous DOA trajectories than conventional neural network models. Full article

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

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