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	2.1	4.0	2019	16.3 Days	CHF 1600	Submit
Aerospace aerospace	2.6	3.0	2014	22.3 Days	CHF 2400	Submit
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Journal of Marine Science and Engineering jmse	2.9	3.7	2013	15.4 Days	CHF 2600	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit
Vehicles vehicles	2.2	2.9	2019	22.2 Days	CHF 1600	Submit
Modelling modelling	-	-	2020	15.8 Days	CHF 1000	Submit

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Published Papers (2 papers)

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All Acoustics Aerospace Applied Sciences JMSE Remote Sensing Vehicles Modelling

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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 Xie

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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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

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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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