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Due to the influence of the complex underwater environment, the sound speed constantly changes, resulting in the acoustic signal propagation trajectory being curved, which greatly affects the positioning accuracy of the underwater long baseline (LBL) system. In this paper, an improved LBL positioning method based on a B-spline surface for fitting the effective sound speed table (ESST) is proposed. Firstly, according to the underwater sound speed profile, the discrete ESST of each measurement station is constructed before the positioning test, and then, the node position of the B-spline surface is optimized by particle swarm optimization (PSO) to accurately fit the discrete ESST. Based on this, the improved LBL positioning method is constructed. In the underwater positioning test, the effective sound speed can be quickly found by measuring the time of arrival (TOA) of the acoustic signal and the target depth, and moreover, the target position parameters can be quickly and accurately estimated. The numerical simulation results show that the improved positioning method proposed in this paper can effectively improve the LBL positioning accuracy and provide the theoretical basis and the technical support for the underwater navigation and positioning. [ABSTRACT FROM AUTHOR]

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency−domain multi−sub−band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal−to−noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets. [ABSTRACT FROM AUTHOR]

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance–density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system. [ABSTRACT FROM AUTHOR]

In order to fulfill the practical application demands of precisely localizing underwater vehicles using passive electric field localization technology, we propose a scalar-potential-based method for the passive electric field localization of underwater vehicles. This method is grounded on an intelligent differential evolution algorithm and is particularly suited for use in three-layer and stratified oceanic environments. Firstly, based on the potential distribution law of constant current elements in a three-layer parallel stratified ocean environment, the mathematical positioning model is established using the mirror method. Secondly, the differential evolution (DE) algorithm is enhanced with a parameter-adaptive strategy and a boundary mutation processing mechanism to optimize the key objective function in the positioning problem. Additionally, the simulation experiments of the current element in the layered model prove the effectiveness of the proposed positioning method and show that it has no special requirements for the sensor measurement array, but the large range and moderate number of sensors are beneficial to improve the positioning effect. Finally, the laboratory experiments on the positioning method proposed in this paper, involving underwater simulated current elements and underwater vehicle tracks, were carried out successfully. The results indicate that the positioning method proposed in this paper can achieve the performance requirements of independent initial value, strong anti-noise capabilities, rapid positioning speed, easy implementation, and suitability in shallow sea environments. These findings suggest a promising practical application potential for the proposed method. [ABSTRACT FROM AUTHOR]

In this work, the performance of coded continuous phase modulation (CPM) transceivers for orthogonal frequency-division multiplexing (OFDM) in underwater acoustic channels is evaluated. The proposed technique employs the Bose–Chaudhuri–Hocquenghem (BCH) class of cyclic codes, and a CPM-based mapper is being used in place of a traditional OFDM mapper. Bit-error-rates (BERs) for various distances are evaluated. We use the Bellhop acoustic channel simulator that is considered to be very close to a real underwater channel. The performance of the proposed system is evaluated for 23 values of the CPM modulation index h as a function of the distance between the transmitter (Tx) and receiver (Rx). Based upon the error performance, we identified several best-performing CPM indices. We also observed that for Tx–Rx distances of 100 and 250 m, the proposed system gives acceptable performance without the need for equalization. We also compared the out-of-band radiation of the proposed system with PSK-OFDM and observed that the uncoded CPM-OFDM has a better out-of-band (OOB) performance than the traditional OFDM. Moreover, a comparison of BERs with the traditional PSK-OFDM system shows that the proposed system outperforms the traditional OFDM system by a wide margin. [ABSTRACT FROM AUTHOR]

Active sonar can usually only directly measure the distance and bearing information of underwater targets, and cannot directly obtain target velocity, acceleration and other information. Therefore, the amount of information is relatively small, making it difficult to support the construction of complex motion models. At the same time, the motion state of underwater maneuvering targets is changeable. In response to the problem of detecting and tracking underwater moving targets by active sonar, this paper proposes a target transient model correction (TMC) filtering tracking method. Based on the conventional Kalman filter (KF) estimation, residual covariance is used as a signal quantity. When there is a large change in it, a transient filter with constant gain is adopted to filter the measurement value. The filtered output is used to correct the KF gain matrix and the target motion state model, to avoid the problem of increasing or even diverging KF estimation errors caused by changes in process noise. Using this method can solve the problem of maintaining stability and filtering estimation accuracy of active sonar tracking of underwater maneuvering targets with less computational and engineering costs. [ABSTRACT FROM AUTHOR]

A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its own electromagnetic field characteristics will have an impact on signal detection. To study the applicability of electric field sensors and magnetic field sensors on wave glider platforms, the structural characteristics of the wave glider were analyzed, and the installation positions of electric field sensors and magnetic field sensors were designed based on the different motion states of the water surface mother body and underwater towing body. The measured electromagnetic field data of the wave glider platform were measured, and the measured data were analyzed. It was determined that the interference electric field energy under typical working conditions of the wave glider was mainly concentrated within 1 Hz, which decreased with increasing frequency, and the magnitude was mV/m. The magnitude of the interference magnetic field is several tens of nT, indicating that the electromagnetic field interference is significant during the working state of the wave glider. Installing an electric field sensor directly at the bottom of the wave glider will cause significant noise interference, while installing the magnetic field sensor directly at the bottom of the tractor will affect the servo and the shaking-induced magnetic field. Moreover, wave gliders should not use electric field signals below 1 Hz as signal sources, but they can utilize axial frequency electromagnetic fields to detect weak electromagnetic signals underwater. [ABSTRACT FROM AUTHOR]

The spatial-temporal distribution of underwater sound speed plays a critical role in determining the propagation mode of underwater acoustic signals. Therefore, rapid estimation and prediction of sound speed distribution are imperative for facilitating underwater positioning, navigation, and timing (PNT) services. While sound speed profile (SSP) inversion methods offer quicker response times compared to direct measurement methods, these methods often focus on constructing spatial sound velocity fields and heavily rely on sonar observation data, thus imposing stringent requirements on data sources. To delve into the temporal distribution pattern of sound speed and achieve SSP prediction without relying on sonar observation data, we introduce the hierarchical long short-term memory (H-LSTM) neural network for SSP prediction. Our method enables the estimation of sound speed distribution without the need for on-site data measurement, significantly enhancing time efficiency. Compared to other state-of-the-art approaches, the H-LSTM model achieves a root mean square error (RMSE) of less than 1 m/s in predicting monthly average sound speed distribution. Its prediction accuracy has improved several-fold over alternative methods, which validates the robust capability of our proposed model in predicting SSP. [ABSTRACT FROM AUTHOR]

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet. [ABSTRACT FROM AUTHOR]

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture. [ABSTRACT FROM AUTHOR]

Simulating the acoustic field excited by pulse sound sources holds significant practical value in computational ocean acoustics. Two primary methods exist for modeling underwater acoustic propagation in the time domain: the Fourier synthesis technique based on frequency decomposition and the time-domain underwater acoustic propagation model (TD-UAPM). TD-UAPMs solve the wave equation in the time domain without requiring frequency decomposition, providing a more intuitive explanation of the physical process of sound energy propagation over time. However, time-stepping numerical methods can accumulate numerical errors, making it crucial to improve the algorithm's accuracy for TD-UAPMs. Herein, the time-domain wavenumber integration model SPARC was improved by replacing the second-order finite element method (FEM) with the high-order accuracy finite difference method (FDM). Furthermore, the matched interface and boundary (MIB) method was used to process the seabed more accurately. The improved model was validated using three classic underwater acoustic benchmarks. By comparing the acoustic solutions obtained using the FDM and the FEM, it is evident that the improved model requires fewer grid points while maintaining the same level of accuracy, leading to lower computational costs and faster processing compared to the original model. [ABSTRACT FROM AUTHOR]

When an airborne sound source is in rapid motion, the acoustic signal detected by the underwater sensor experiences a substantial Doppler shift. This shift is intricately linked to the motion parameters of the sound source. Analyzing the Doppler shift characteristics of received acoustic signals enables not only the estimation of target motion parameters but also the localization of the airborne sound source. Currently, the predominant methods for estimating parameters of uniformly moving targets are grounded in classical approaches. In this study, the application of the Doppler warping transform, traditionally applicable to sound sources in uniform linear motion, is extended to encompass a broader spectrum of sound source trajectories. Theoretical and experimental data validate the efficacy of this transform in linearizing the Doppler shift induced by a source in curved motion. Building upon this foundation, a methodology is proposed for locating airborne acoustic sources in curved motion from underwater. Sea experimental data corroborate the method's effectiveness in achieving underwater localization of a helicopter target during curved motion. [ABSTRACT FROM AUTHOR]

The parabolic wave equation describes wave propagation in a preferable direction, which is usually horizontal in underwater acoustics and vertical in seismic applications. For dense receiver arrays (receiver spacing is less than signal wavelength), this equation can be used for propagating the recorded wavefield back into the medium for imaging sources and scattering objects. Similarly, for multiple source and receiver array acquisition, typical for seismic exploration and potentially beneficial for ocean acoustics, one can model data in one run using an extension of the parabolic equation—the pseudo-differential Double Square Root (DSR) equation. This extended equation allows for the modeling and imaging of multi-source data but operates in higher-dimensional space (source, receiver coordinates, and time), which makes its numerical computation time-consuming. In this paper, we apply a faster ray method for solving the DSR equation. We develop algorithms for both kinematic and dynamic ray tracing applicable to either data modeling or true-amplitude recovery. Our results can be used per se or as a basis for the future development of more elaborated asymptotic techniques that provide accurate and computationally feasible results. [ABSTRACT FROM AUTHOR]

Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity. [ABSTRACT FROM AUTHOR]

Underwater sound speed is one of the most significant factors that affects high-accuracy underwater acoustic positioning and navigation. Due to its complex temporal variation, the forecasting of the underwater sound speed field (SSF) becomes a challenging task. Taking advantage of machine learning methods, we propose a new method for SSF forecasting based on the least square support vector machine (LSSVM) and a multi-parameter model, aiming to enhance the forecasting accuracy of underwater SSF with hourly resolution. We first use a matching extension method to standardize profile data and train the LSSVM with the parameters of observation time, temperature, salinity, and depth. We then employ radial basis function kernels to construct the forecasting model of SSF. We validate the feasibility and effectiveness of the LSSVM model by comparing it with the polynomial fitting (PF) and back propagation neural network (BPNN) methods, using hourly data obtained from the measured data and open data. The results show that the means of the root mean square for the LSSVM based on the observation time parameter and the LSSVM based on the multi-parameter model achieve 0.51 m/s and 0.45 m/s, respectively, presenting a significant improvement compared with the PF (0.82 m/s) and BPNN (0.76 m/s) methods. [ABSTRACT FROM AUTHOR]

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper's method are verified by computer simulation and field testing. [ABSTRACT FROM AUTHOR]

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation. [ABSTRACT FROM AUTHOR]

The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra. [ABSTRACT FROM AUTHOR]

Stable communication technologies in complex waters are a prerequisite for underwater operations. Underwater acoustic communication is susceptible to multipath interference, while underwater optical communication is susceptible to environmental impact. The underwater electric field communication established based on the weak electric fish perception mechanism is not susceptible to environmental interference, and the communication is stable. It is a new type of underwater communication technology. To address issues like short communication distances and high bit error rates in existing underwater electric field communication systems, this study focuses on underwater electric field communication systems based on direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation techniques. To verify the feasibility of the established spread spectrum electric field communication system, static communication experiments were carried out in a swimming pool using the DSSS-based system. The experimental results show that in fresh water with a conductivity of 739 μS/cm, the system can achieve underwater current electric field communication within a 11.2 m range with 10−6 bit errors. This paper validates the feasibility of DSSS BPSK in short-range underwater communication, and compact communication devices are expected to be deployed on underwater robots for underwater operations. [ABSTRACT FROM AUTHOR]

Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the unique features of ship radiation noise. This study proposes a novel cross-domain contrastive learning-based few-shot (CDCF) method for UATR to alleviate overfitting issues. This approach leverages self-supervised training on both source and target domains to facilitate rapid adaptation to the target domain. Additionally, a base contrastive module is introduced. Positive and negative sample pairs are generated through data augmentation, and the similarity in the corresponding frequency bands of feature embedding is utilized to learn fine-grained features of ship radiation noise, thereby expanding the scope of knowledge in the source domain. We evaluate the performance of CDCF in diverse scenarios on ShipsEar and DeepShip datasets. The experimental results indicate that in cross-domain environments, the model achieves accuracy rates of 56.71%, 73.02%, and 76.93% for 1-shot, 3-shot, and 5-shot scenarios, respectively, outperforming other FSL methods. Moreover, the model demonstrates outstanding performance in noisy environments. [ABSTRACT FROM AUTHOR]

This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI's superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF). [ABSTRACT FROM AUTHOR]

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network's performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods. [ABSTRACT FROM AUTHOR]

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage. [ABSTRACT FROM AUTHOR]

Fractal dimension, as a common nonlinear dynamics metric, is extensively applied in biomedicine, fault diagnosis, underwater acoustics, etc. However, traditional fractal dimension can only analyze the complexity of the time series given a single channel at a particular scale. To characterize the complexity of multichannel time series, multichannel information processing was introduced, and multivariate Higuchi fractal dimension (MvHFD) was proposed. To further analyze the complexity at multiple scales, multivariate multiscale Higuchi fractal dimension (MvmHFD) was proposed by introducing multiscale processing algorithms as a technology that not only improved the use of fractal dimension in the analysis of multichannel information, but also characterized the complexity of the time series at multiple scales in the studied time series data. The effectiveness and feasibility of MvHFD and MvmHFD were verified by simulated signal experiments and real signal experiments, in which the simulation experiments tested the stability, computational efficiency, and signal separation performance of MvHFD and MvmHFD, and the real signal experiments tested the effect of MvmHFD on the recognition of multi-channel mechanical signals. The experimental results show that compared to other indicators, A achieves a recognition rate of 100% for signals in three features, which is at least 17.2% higher than for other metrics. [ABSTRACT FROM AUTHOR]

With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications. [ABSTRACT FROM AUTHOR]

Increased turbulent flow and sediment transport during flood or hydropeaking events often induces rapid changes in underwater sound pressure levels, which is here referred to as soundpeaking. This study is the first to investigate such a change in the underwater soundscape in relation to fish behavior using an experimental approach. Trials were conducted in an experimental channel stocked with either adult chub (Squalius cephalus) or brown trout (Salmo trutta). To mimic soundpeaking, the underwater soundscape of a small alpine river was recorded during a flood event and later played back through an underwater speaker during treatment trials. Furthermore, trials were recorded with a video camera, and based on the fish position, movement variables (swimming distance, number of movement direction changes, variance of the acceleration), the aggregation of individuals, the longitudinal and the lateral position in the experimental area were compared between control (no sound played) and treatment trials. During treatment trials, brown trout changed their movement direction significantly more often, chub showed a significantly higher variation of the acceleration, and individuals from both species were significantly more aggregated. Furthermore, the soundpeaking treatment had a significant effect on the longitudinal position of brown trout in the experimental area. However, the overall results did not provide any indication for a stronger soundpeaking effect in chub despite being equipped with much more refined hearing abilities in comparison to brown trout. Based on these results and findings from other studies, soundpeaking is discussed as a behavioral trigger as well as a source of acoustic stress. [ABSTRACT FROM AUTHOR]

The current state of the art in hydroacoustics research employs a variety of feature extraction techniques with the goal of accurately classifying a ship based on its radiated noise. These techniques are capable of accuracy in excess of 95%. A question arises as to whether similar techniques could be applied to a known vessel to identify and monitor individual systems from the ship's noise. In this paper, the fast orthogonal search algorithm is used as a basis for a feature extraction and classification algorithm. This algorithm is applied to real recordings of ship-radiated noise and is shown to be capable of identifying the running status of a subset of the ship's systems, providing a proof of concept for the detection and monitoring of a ship's systems based solely on the ships hydroacoustic noise. [ABSTRACT FROM AUTHOR]

The acoustic absorption characteristics of anechoic coatings attached to the surface of underwater vehicles are closely related to their acoustic stealth. Owing to the essential property of local resonance, the narrow sound-absorption band cannot meet the underwater broadband sound absorption requirements. To this end, a multi-mechanism synergistic composite acoustic structure (MMSC−AS) was designed according to the integration of multiple acoustic dissipation mechanisms in this paper. Then, the acoustical calculation model for MMSC−AS was developed by using the graded finite element method (G-FEM), and the feasibility and the correctness of the established acoustical calculation model were verified. The underwater sound absorption behaviors of MMSC−AS were studied, and the optimization of the sound absorption characteristics of the MMSC−AS was also carried out. The results indicated that the calculation accuracy of the G-FEM was better than that of the FEM under the condition of the same mesh elements. Moreover, there were many sound wave regulation mechanisms in the MMSC−AS, and the synergy between the mechanisms enriched the mode of sound acoustic energy dissipation, which could widen the absorption band with effect. This study provides theoretical and technical basis for breaking through the challenge of low-frequency and broadband acoustic structure design of underwater vehicles. [ABSTRACT FROM AUTHOR]

This paper proposes an underwater broadband target depth estimation method based on the multipath arrival structure in medium and short-range deep-sea environments. The proposed approach involves separating the multipath rays arriving at the vertical line array using the matched filtering technique. The combined beamforming is then applied to the vector hydrophone vertical array (VHVA) to obtain more accurate elevation angle estimates. The components of the interference sound field at different distances are judged, and the signal received by the array is compensated using suitable estimated angles. Finally, the multipath time delay difference is extracted from the pulse peaks of the compensated signal, and the target depth is estimated by establishing the relationship among the multipath time delay difference, the elevation angle and depth. Simulations and experiments demonstrate the effectiveness of this method in reducing the depth estimation bias and avoiding the misjudgment of surface or submerged targets. [ABSTRACT FROM AUTHOR]

The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation. [ABSTRACT FROM AUTHOR]

This study aimed to examine the acoustic field radiated by propellers and underwater vehicles. For the verification of sound radiation in underwater vehicles, numerical methods are widely used in addition to experiments and propeller blade frequencies for calculation and validation. Numerical convergence and accuracy are more important for near-field and far-field problems. This paper uses the boundary element method (BEM) to assess the convergence of the finite volume method (FVM). In this study, the FVM, including the Reynolds-averaged Navier–Stokes method and the Ffowcs Williams–Hawkings (FW-H) acoustic model, is used to investigate the influence of various geometric inflows on the hydrodynamic and noise performance of the propeller. Then, the sound radiation of the FVM is compared with the BEM at the far field to determine the number of meshed elements. Furthermore, spectral analysis is being conducted to examine the noise generated by the underwater vehicle and propeller. The objective is to investigate the influence of the free surface on propeller efficiency. After verifying the numerical simulation, the results indicate that a relationship can be established between water pressure and propeller thrust under specific conditions. This relationship can be used to estimate the magnitude of propeller thrust at different water depths. The simulated results of propeller thrust, torque coefficient, propulsion efficiency, and sound radiation in this study are consistent with experimental values. This demonstrates the accuracy and practicality of the findings of numerical procedures in engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time–frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference between two overlapping multiple whistles while maintaining a high data rate. The data rate and bit error rate (BER) performance of the proposed method were compared with conventional underwater covert communication through an additive white Gaussian noise channel, a modeled underwater channel, and practical ocean experiments. For the covertness test, the similarity of the proposed multiple whistles was compared with the real dolphin group whistles using the mean opinion score test. As a result, the proposed method demonstrated a higher data rate, better BER performance, and large covertness to the real dolphin group whistles. [ABSTRACT FROM AUTHOR]

The acoustic properties of seafloor sediments have always been important parameters in sound field analyses and exploration for marine resources, and the accurate acquisition of the acoustic properties of sediments is one of the difficulties in the study of underwater acoustics. In this study, sediment cores were taken from the northern South China Sea, and the acoustic properties were analyzed. Since traditional methods (such as regression equations or theoretical models) are difficult to apply in practical engineering applications, we applied remote sensing data to sound velocity prediction models for the first time. Based on the influencing mechanism of the acoustic properties of seafloor sediments, the sediments' source, type and physical properties have a great influence on the acoustic properties. Therefore, we replaced these influencing factors with easily accessible data and remote sensing data, such as parameters of granularity, distance to the nearest coast, decadal average sea surface productivity, water depth, etc., using deep neural networks (DNN) to develop a sound velocity prediction model. Compared with traditional mathematical analyses, the DNN model improved the accuracy of prediction and can be applied to practical engineering applications. [ABSTRACT FROM AUTHOR]

Background: Upper extremity arthroscopic surgery is a highly technique-dependent procedure that requires the surgeon to assess difficult cartilage conditions and manage the risk of iatrogenic damage to nerves and vessels adjacent to the joint capsule in a confined joint space, and a device that can safely assist in this procedure has been in demand. Methods: In this study, we developed a small intra-articular ultrasound (AUS) probe for upper extremity joint surgery, evaluated its safety using underwater sound field measurement, and tested its visualization with a phantom in which nerves and blood vessels were embedded. Results: Sound field measurement experiments confirmed the biological safety of the AUS probe's output, while confirming that sufficient output power level performance was obtained as an ultrasound measurement probe. In addition, images of blood vessels and nerves were reconstructed discriminatively using A-mode imaging of the agar phantom. Conclusions: This study provides proof-of-concept of the AUS probe in upper extremity surgery. Further studies are needed to obtain approval for use in future medical devices. [ABSTRACT FROM AUTHOR]

The precision of acoustic ranging and navigation depends on the accuracy of the information about the sound speed field in the area of interest. Large-scale inhomogeneities in the bottom relief and water column can significantly affect the horizontal rays corresponding to vertical modes (in the framework of Burridge–Weinberg formalism), which can lead to delays in the acoustic signal modal components, as compared to propagation along the geodesics on the Earth's surface. In this study, the influence of horizontal refraction on the delay times of the modal components is considered. In particular, it is studied to what extent the presence of a synoptic eddy near the source–receiver path increases the effective propagation distances due to horizontal refraction. The elongation of horizontal eigenrays relative to the geodesic connecting the source and the receiver is also estimated. The influence of hydrological inhomogeneities on the propagation time of different modal components of a broadband acoustic signal is investigated. This is accomplished by the integration of the group slowness (reciprocal to the group speed) along the horizontal eigenrays connecting the locations of the source and the receiver. Implications for improving the accuracy of the solution of acoustic ranging problems are discussed. [ABSTRACT FROM AUTHOR]

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to reveal the structural impact performance of PMMA materials in underwater engineering thoroughly, this paper firstly proposed a simplified plate model for a spherical shell hull under concentrative impact loading. Then, to simulate the hyper-elastic material properties of PMMA in the impact process, the Johnson–Cook constitutive model and damage failure model were adopted. And the least squares method was used to confirm accurately the J–C constitutive and damage failure model parameters of PMMA through material test data. Moreover, the dynamic process of the steel bullet impacting the PMMA plate structure was analyzed by the finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper have a good convergence, and the residual velocities at different initial velocities and thicknesses of plates are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J–C constitutive and damage failure models in this paper are verified accordingly. Finally, based on the presented finite element model, the structure response and the variation of residual velocity of the bullet with the PMMA plate thickness was analyzed in depth; that is, the results show that the residual velocity of the bullet has a certain linear relationship with the thickness, even in an underwater environment, and even in an underwater environment will increase both with a thicker structure or a higher pressure. [ABSTRACT FROM AUTHOR]

At present, some shortcomings of the research on coupling modeling of the oceanic front–sound field may need attention: (1) Most of the acoustic propagation simulation is based on ideal front models, but the application of investigated marine data is lacking. (2) Most studies focus on the acoustic field characteristics, with the influence of fronts on acoustic propagation, but few studies aim at the direct quantitative analysis of the performance of underwater acoustic detection in oceanic fronts. To deal with the above problems, based on the measured data in the northwest Pacific Ocean, here, we first design different sound source layout schemes and calculate sound field characteristics in the sub-Arctic front using the ray theory. Then, the cumulative detection probability model is built based on the active sonar equation to evaluate the efficiency of underwater detection. Finally, the detection probability is calculated and expressed by regionalization, and the influence of the sub-Arctic front on underwater detection is quantitatively analyzed. The results show that the sub-Arctic front can significantly affect the underwater acoustic detection. The sound source located in the front in the cold-water mass has a better detection performance, especially detecting towards cold water (horizontal detection range > 60 km). In contrast, the sound source located in the warm-water mass has a poor detection performance (horizontal detection range in shallow sea < 10 km). [ABSTRACT FROM AUTHOR]

Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential. [ABSTRACT FROM AUTHOR]

This article discusses the results obtained from performing a test acoustic-hydrological experiment in August 2022 at a marine test site from the coast of Sakhalin Island to the Kita-Yamato Bank in the Sea of Japan. A methodology for preliminary studies in the water area is presented. It is designed to study the climatic variability of the temperature regimes of the aquatic environment based on numerical modeling using the RAY computer program and the NEMO ocean hydrodynamic circulation model. One of the main results is the value of the average temperature of the marine environment calculated with high accuracy on the axis of the underwater sound channel in the Sea of Japan on a thousand-kilometer acoustic path when crossing the vortex system. The appearance of the measuring system, technical means, and methods described in the article can be used as the basis for organizing high-precision operational monitoring of thermodynamic processes in extended marine areas. [ABSTRACT FROM AUTHOR]

Underwater noise classification is of great significance for identifying ships as well as other vehicles. Moreover, it is helpful in ensuring a marine habitat-friendly, noise-free ocean environment. But a challenge we are facing is the small-sized underwater noise samples. Because noise is influenced by multiple sources, it is often difficult to determine and label which source or which two sources are dominant. At present, research to solve the problem is focused on noise image processing or advanced computer technology without starting with the noise generation mechanism and modeling. Here, a typical underwater noise generation model (UNGM) is established to augment noise samples. It is established by generating noise with certain kurtosis according to the spectral and statistical characteristics of the actual noise and filter design. In addition, an underwater noise classification model is developed based on UNGM and convolutional neural networks (CNN). Then the UNGM-CNN-based model is used to classify nine types of typical underwater noise, with either the 1/3 octave noise spectrum level (NSL) or power spectral density (PSD) as the input features. The results show that it is effective in improving classification accuracy. Specifically, it increases the classification accuracy by 1.59%, from 98.27% to 99.86%, and by 2.44%, from 97.45% to 99.89%, when the NSL and PSD are used as the input features, respectively. Additionally, the UNGM-CNN-based method appreciably improves macro-precision and macro-recall by approximately 0.87% and 0.83%, respectively, compared to the CNN-based method. These results demonstrate the effectiveness of the UNGM established in noise classification with small-sized samples. [ABSTRACT FROM AUTHOR]

Underwater images are widely used in ocean resource exploration and ocean environment surveillance. However, due to the influence of light attenuation and noise, underwater images usually display degradation phenomena such as blurring and color deviation; an enhancement method is required to make the images more visible. Currently, there are two major approaches for image enhancement: the traditional methods based on physical or non-physical models, and the deep learning method. Inspired by the fusion-based idea, this paper attempts to combine traditional methods with deep learning and proposes a multi-input dense connection generator network (MDNet) for underwater image enhancement. Raw images and processed images are input into the network together, the shallow information is fully utilized by dense connection, and the network is trained in generative and adversarial manner. We also design a multiple loss function to improve the visual quality of the generated images. We conduct both qualitative and quantitative experiments, and then compare the results with state-of-the-art approaches comprehensively using three representative datasets. Results show that the proposed method can effectively improve the perceptual and statistical quality of underwater images. [ABSTRACT FROM AUTHOR]

Underwater acoustic spiral sources can generate spiral acoustic fields where the phase depends on the bearing angle. This allows estimating the bearing angle of a single hydrophone relative to a single source and implementing localization equipment, e.g., for target detection or unmanned underwater vehicle navigation, without requiring an array of hydrophones and/or projectors. A spiral acoustic source prototype made out of a single standard piezoceramic cylinder, which is able to generate both spiral and circular fields, is presented. This paper reports the prototyping process and the multi-frequency acoustic tests performed in a water tank where the spiral source was characterized in terms of the transmitting voltage response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and showed a maximum angle error of 3° when the calibration and the operation were carried out in the same conditions and a mean angle error of up to 6° for frequencies above 25 kHz when the same conditions were not fulfilled. [ABSTRACT FROM AUTHOR]

Rapid improvements in underwater vehicle technology have led to a significant increase in the demand for underwater sound absorption materials. These materials, unlike their counterparts utilized in air, must have high hydrostatic pressure resistance, corrosion resistance, and other advantageous attributes. This necessitates the development of innovative, composite sound-absorbing materials with multifunctional properties, which presents substantial challenges for researchers. In this comprehensive review, we systematically analyze and categorize the mechanisms governing underwater sound absorption, hydrostatic pressure resistance, and corrosion prevention while considering related research advances. Furthermore, we provide an extensive overview of research advancements, existing challenges, and potential solutions pertaining to multifunctional and integrated underwater sound-absorbing materials. This review aims to serve as a valuable resource for future investigations into the development and optimization of multifunctional integrated underwater sound-absorbing materials, thereby contributing to the advancement of underwater vehicle technology. [ABSTRACT FROM AUTHOR]

Thus, using acoustic conditioning can achieve the purpose of attracting dispersed individual fish into a group through sound signals, to effectively control fish behavior, and enable the catching of fish with fishing gear. They documented that in the mainstream of the Three Gorges Reservoir and its tributary, despite differences in their morphometric characteristics and fish species, the two types of acoustic surveys (parallel and zigzag transects) yielded fully comparable results, i.e., fish density and size distribution estimates were statistically the same. Understanding fish migration patterns and habitat use is essential for fisheries management, as fish movement data provide knowledge of space-use patterns in geographic and/or environmental spaces and link home ranges and habitat selection. Consequently, knowledge of the temporal and spatial movement and habitat use of target fish species during key life history stages is critical for fish population/community management, aquatic ecosystem conservation, and health improvement. [Extracted from the article]

The detection and localization of acoustic sources remain technological challenges in bioacoustics, in particular, the tracking of moving underwater sound sources with a portable waterproof tool. For instance, this type of tool is important to describe the behavior of cetaceans within social groups. To contribute to this issue, an original innovative autonomous device, called a CETOSCOPE©, was designed by ABYSS NGO, including a 360° video camera and a passive acoustic array with 4 synchronized hydrophones. Firstly, different 3D structures were built and tested to select the best architecture to minimize the errors of the localizations. Secondly, a specific software was developed to analyze the recorded data and to link them to the acoustic underwater sources. The 3D localization of the sound sources is based on time difference of arrival processing. Following successful simulations on a computer, this device was tested in a pool to assess its efficiency. The final objective is to use this device routinely in underwater visual and acoustic observations of cetaceans. [ABSTRACT FROM AUTHOR]

With the development of underwater navigation and underwater communication, it remains difficult to obtain time delay measurements after propagating long distance. This paper proposes an improved high-accuracy time delay measuring method for long distance underwater channel propagation. First, by sending an encoded signal, the signal acquisition is carried out at the receiving end. Then, to improve signal to noise ratio (SNR), bandpass filtering is carried out on the receiving end. Next, considering the random changes in the underwater sound propagation channel, a strategy is proposed to select the optimal time window for cross-correlation. Then, new regulations are proposed to calculate the cross-correlation results. To verify the effectiveness of the algorithm, we compared it with other algorithms under low SNR conditions using Bellhop simulation data. Finally, the accurate time delay is obtained. With underwater experiments over different distances, the method proposed by the paper achieves high accuracy. The error is about 10−3 s. The proposed method makes a contribution to underwater navigation and communication. [ABSTRACT FROM AUTHOR]

Fine-grained ship-radiated noise recognition methods of different specific ships are in demand for maritime traffic safety and general security. Due to the high background noise and complex transmission channels in the marine environment, the accurate identification of ship radiation noise becomes quite complicated. Existing ship-radiated noise-based recognition systems still have some shortcomings, such as the imperfection of ship-radiated noise feature extraction and recognition algorithms, which lead to distinguishing only the type of ships rather than identifying the specific vessel. To address these issues, we propose a fine-grained ship-radiated noise recognition system that utilizes multi-scale features from the amplitude–frequency–time domain and incorporates a multi-scale feature adaptive generalized network (MFAGNet). In the feature extraction process, to cope with highly non-stationary and non-linear noise signals, the improved Hilbert–Huang transform algorithm applies the permutation entropy-based signal decomposition to perform effective decomposition analysis. Subsequently, six learnable amplitude–time–frequency features are extracted by using six-order decomposed signals, which contain more comprehensive information on the original ship-radiated noise. In the recognition process, MFAGNet is designed by applying unique combinations of one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) networks. This architecture obtains regional high-level information and aggregate temporal characteristics to enhance the capability to focus on time–frequency information. The experimental results show that MFAGNet is better than other baseline methods and achieves a total accuracy of 98.89% in recognizing 12 different specific noises from ShipsEar. Additionally, other datasets are utilized to validate the universality of the method, which achieves the classification accuracy of 98.90% in four common types of ships. Therefore, the proposed method can efficiently and accurately extract the features of ship-radiated noises. These results suggest that our proposed method, as a novel underwater acoustic recognition technology, is effective for different underwater acoustic signals. [ABSTRACT FROM AUTHOR]

Underwater radiated noise is part of the anthropogenic emissions into the environment and as such a pressing problem for the preservation of the marine ecosystem. In order to direct attention to the most relevant noise sources associated with ships it is crucial to precisely determine the local origins of the acoustic emissions. As acoustics are by nature perceived through a very subjective auditory perception, visual post-processing support is required in engineering applications to assess the impact on structures and to create an understanding of the overall noise field geometrically, topologically, and directionally. In the context of CFD simulations, this may be achieved by considering the pressure pulses on domain boundary surfaces or passive surfaces, or by evaluating various volumetric information, such as Proudman acoustic sources or the Lighthill stress tensor, which is the fundamental input for many acoustic analogies including the Ffowcs-Williams-Hawkings method. For a propeller-hull configuration, the acoustic emissions from modeled and scale-resolved turbulence two-phase CFD analyses are evaluated in detail with different visualization methods. It is shown that the spatial distribution information of frequency domain pressure pulses, and the corresponding complex phase angles on specific passive geometries, as well as the Lighthill stress tensor may be utilized to create a better understanding of underwater acoustics. This allows the identification of source types and their respective excitation of the hull and emission characteristics of the hydrodynamic sources into the fluid domain, as well as the effect of the CFD simulation domain geometry extent. [ABSTRACT FROM AUTHOR]

Underwater noise is a significant pollutant produced by anthropogenic activities carried out in the sea. Several types of acoustic sources can potentially have adverse impacts on marine fauna, especially on cetaceans. The vocalization response of cetaceans to underwater noise varies depending on noise characteristics such as duration, bandwidth, and intensity, as well as the species being insonified. Some studies report changes in vocalization properties due to continuous noise, but there is a lack of knowledge regarding impulsive noise sources, especially those related to explosive events. It is known that underwater explosions represent a serious threat to marine fauna because it produces one of the highest sound pressure levels introduced by anthropogenic activities. In this communication, an opportunistic study related to changes in the dolphin vocalizations was performed by considering two scenarios (i.e., before and after a detonation event). The acoustic raw data were recorded by a passive acoustic device installed in a mooring line deployed in the Mediterranean coast of Spain. The objective of the experimental installation was to monitor the underwater sound pressure level in the framework of the development of the Marine Strategy Framework Directive (MSFD) in Spain. A detonation event of unknown origin was recorded during the monitoring period while Delphinids were vocalizing, allowing for the observation of their acoustic reaction to the explosion. The study considers the number of vocalizations, morphology of whistles, and spectral characteristics before and after the explosion. The results obtained indicate that the number of whistles, their complexity in terms of morphology, and spectral components vary due to the explosive event, showing significant differences that will be presented and discussed in this communication. [ABSTRACT FROM AUTHOR]