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Non-cooperative underwater acoustic signal detection and recognition technologies occupy an important position in the marine communication environment. Traditional methods require threshold adjustments based on the environment, making the application less robust. This article addresses the technical challenges of automatic detection and recognition of non-cooperative underwater communication signals. It utilizes two pre-trained models, SSD(Single Shot Detector)-MobileNet-V2-FPNlite(Feature Pyramid Network lite) and EfficientDet, which can simultaneously perform signal classification and automatic drawing of signal bounding boxes on the time-frequency graph. SSD-MobileNet-V2-FPNlite uses the Linear Bottleneck layers and the Inverted Residual structure, which improve the capture of multi-scale representations of signals. EfficientDet performs bidirectional feature fusion to enhance the connections and information flow between signal features. After fine-tuning with simulated data, SSD-MobileNet-V2-FPNlite achieves high detection accuracy on both simulated and real lake test data without transfer learning. EfficientDet also performs well on simulated data and, after transfer learning, shows high accuracy in recognizing and drawing bounding boxes on the lake test data. Both methods exhibit strong generalization ability, feature fewer model parameters, and have lower computational complexity.

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

This work considers the long baseline (LBL) acoustic localization problem. In the classical LBL localization approach, the time of arrival (TOA) of direct sound from each buoy is estimated independently, and the estimated TOAs are then utilized to solve for the target’s position. This approach ignores the fact that all of the estimated TOAs from different buoys correspond to the same target and thus are intrinsically correlated. To address this shortcoming, a novel localization framework based on track-before-detect (TBD) theory is established in this work, which allows the target location to be directly determined. Since it is completely freed from the traditional localization paradigm, some of the persistent and challenging problems faced by classical localization methods, such as direct sound selection and unacceptable false alarm/detection tradeoffs caused by threshold setting, can be circumvented. The effectiveness of the TBD approach is validated on both simulated and real datasets. The experimental results indicate that the proposed approach can track targets continuously and accurately and can be effectively implemented even under challenging conditions. [ABSTRACT FROM AUTHOR]

Underwater acoustic networks have recently risen as an effective support to several marine and oceanic applications. However, the potential presence of a large number of nodes simultaneously connected has pushed the scientific community to address the matter of communication resources management, hence to provide the best possible performance for each link. Since underwater acoustic communications (UWACs) suffer from limited bandwidth and long propagation delays, medium access control results really challenging. The best known schemes, that are time division, frequency division and code division multiple access have been considered to handle this problem in underwater scenarios, even though they suffer from some weaknesses. Furthermore, spatial division multiple access achievable in Multiple-Input Multiple-Output (MIMO) systems has emerged as a promising technique able to fit with the multipath propagation typically characterizing UWACs. Dealing with underwater medium access control, we investigate the feasibility of a novel hybrid multiple access technique that works in a bi-dimensional resources domain, namely space and frequency. This solution is aimed to mitigate the multi-user interference by exploiting spatial diversity and, whenever necessary, by applying frequency reuse. Finally, we discuss the feasibility and potential of massive MIMO paradigm, conveniently recast into the underwater context.

In this brief we present a design of a full-duplex (FD) underwater acoustic communication (UAC) receiver with a two-sensor transducer. The transducer consists of two piezo-ceramic cylinders cast together in polyurethane resin; one acts as a hydrophone and the other a projector. As the distance between the projector and hydrophone is extremely short, a high level of self-interference (SI) from the projector is received at the hydrophone. This SI must be eliminated to enable far-end data demodulation. In this brief, we adopt a digital SI cancellation scheme based on adaptive filtering. We investigate the SI cancellation and far-end data demodulation performance with the two-sensor transducer in tank and lake experiments. The Rake receiver is used for the far-end data demodulation. As demonstrated by the experimental results, the far-end SNR loss in the FD experiment is less than 3 dB compared to the half-duplex experiment when there is only far-end transmission. [ABSTRACT FROM AUTHOR]

Because of their wide application in the underwater environment, underwater acoustic networks (UANs) have received increased attention.However, the intrinsic attributions of low bandwidth, long propagation delay, high bit error rate, and restricted energy bring about great challenges to data transmission in UANs. In this article, we propose a routing protocol for UANs called the fuzzy control-based layering routing (FCLR) protocol. With the FCLR protocol, an underwater node may learn or update its layer and the information of its neighbors from packets overhead before selecting the best forwarding node (BFN). A fuzzy control system (FCS) is used to select the BFN based on the input variables including residual energy and node density of neighbor nodes. Extensive experiments are conducted by the NS3 network simulator to evaluate the performance of the FCLR protocol. The results show that the FCLR protocol has superior performance in terms of packet delivery rate (PDR), endto- end delay (EED), and total energy consumption (TEC). Furthermore, the FCLR protocol is tested in Qinghai Lake, the largest saltwater lake in China, and its performance was evaluated in terms of PDR, throughput, and EED. When nodes are moving, the PDR can reach 92.6% and the throughput can reach 350.64 bps. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) face many security threats since they are deployed in open or even hostile environments. How to ensure the confidentiality of data transmission in the presence of eavesdroppers at unknown locations is an urgent problem. In this article, we first define the security coverage of the network. Then, a physical layer secure transmission scheme, named ST-CJ, which is based on self-assisting nodes with cooperative jamming signals, is proposed. With the allocated jamming transmission power, the proposed scheme takes advantage of the long propagation delays of underwater acoustic (UWA) signals to create collisions at the eavesdropper without affecting the reception of the legitimate user. In the ST-CJ, the transmission schedule and power level of each assisting node are jointly optimized. The goal is to maximize the coverage of the secure area in the network with minimal energy consumption. Both simulation and field test results demonstrate that the proposed scheme obviously reduces the interception ability of eavesdroppers, thus realizing the confidentiality of transmission. [ABSTRACT FROM AUTHOR]

Despite decades-long development, underwater communication systems still cannot achieve high data rates and long communication ranges at the same time (i.e., beyond 1 Mb/s and 1 km). Currently, acoustic communication is the only choice to achieve long distances. However, the inherent low acoustic bandwidth results in extremely low data rates. In this article, the acoustic reconfigurable intelligent surface (RIS) system is proposed to realize high-data-rate long-range underwater communications. Although the EM-based RIS has been widely investigated in terrestrial scenarios in recent years, the underwater acoustic RIS is based on completely different physics principles. Hence, the EM-based terrestrial RISs do not work for underwater acoustic signals. More-over, the long acoustic wave propagation delay, the inherent wideband nature, and the water behavior all impose unique challenges in underwater RIS operation. Therefore, this article presents a new hardware design to realize acoustic RIS, based on which the underwater RIS operation protocols are developed to address the aforementioned challenges. The proposed acoustic RIS system can be considered as an underwater infrastructure that enables beamforming functionalities for all types of devices, especially small robots and low-cost sensors. The simulation results show that the proposed acoustic RIS system can efficiently reflect acoustic waves and dramatically increase communication data rates and distances. [ABSTRACT FROM AUTHOR]

Underwater soundscapes of coastal zones close to human settlements are heterogeneous in nature. Multiple ships and biological sources are often simultaneously present in the passive sonar vicinity. The classification of such heterogeneous underwater soundscapes is a challenging task for humans as well as machine learning systems. In this article, a Bayesian deep learning approach is proposed that can accurately classify multiple ships simultaneously present near the sensor (multilabel classification) and provide uncertainty in the classification. This is achieved by assuming a Bayesian formulation of standard convolutional neural network architecture to not only assign multilabels per inference but also to provide per inference uncertainty. By utilizing almost 3500 h of passive sonar data (spanning more than a year of sensor deployment) labeled through automated fusion with automatic identification system information, both multiclass and multilabel classification tasks of ship-generated noise are addressed. The best performing Bayesian architecture on the multilabel task achieves a weighted $F^{1}$ score of 0.84, where each prediction is accompanied by a measurement of uncertainty, which is used to further enhance the understanding of model predictions. [ABSTRACT FROM AUTHOR]

In this article an autonomous distributed-consensus system is evaluated with regard to its efficacy for establishing a common detection statistic among a network of passive acoustic hydrophones. The notion of consensus among sensors is briefly reviewed, and an algorithm motivated by diffusion of information among network neighbors is developed within the context of spectral graph theory. Relative entropy derived at each node provides information that the network averages to reach a joint detection decision. As the expected value of a log-likelihood ratio, relative entropy is the appropriate detection statistic for this application because it is nondimensional, additive, and insensitive to calibration. These properties facilitate the fusion of information among heterogeneous sensors. Seven nodes of an acoustical receiver network were moored on the New Jersey coastal shelf in May 2019, while towed and autonomous sources provided target signals at 900–1050 Hz. The convergence rate of the system to a detection statistic was found to be limited by slow acoustic communications, and convergence to a global consensus was often disrupted by communication breakdowns. When failures occurred, the network successfully demonstrated robustness by dissociating into viable subnetworks, each of which converged to a valid detection statistic. [ABSTRACT FROM AUTHOR]

To evaluate the performance of underwater acoustic communication systems, it is typically assumed that the noise at the receiver is uncorrelated spatially and temporally. This assumption underestimates the impact of acoustic ocean ambient noise on the performance of communication systems. In this article, the impact of ocean ambient noise on a coherent acoustic communication system is analyzed. The communication performance is assessed in narrowband conditions at a center frequency of ${\text{2.048}\;\text{kHz}}$ using the noise measurements from two different experiments—DalComm1 and the shallow water Canada Basin acoustic propagation experiment (SW CANAPE). DalComm1 focuses on characterizing the acoustic channel over ranges of 1–10 km on the Nova Scotian littoral, while the spatio-temporal variability of noise propagation in shallow and deep water environments was characterized during the CANAPE experiments. The ambient noise coherence and directionality in both environments were also measured. Two distinct noise modeling methodologies are presented to represent realistic synthetic ambient noise with defined directionality. Further, the synthetic noise is validated against measured ambient noise. The impact of ambient noise characteristics on an optimum space-time filter is characterized. A frame structure with an optimum training duration is also defined for the adaptive filter. It is observed that the bit-error rate of the space-time filter depends on optimizing the training and payload duration in the received signal. [ABSTRACT FROM AUTHOR]

In this article, we propose an iterative receiver based on gridless variational Bayesian line spectra estimation (VALSE), named JCCD-VALSE, that jointly estimates the carrier frequency offset (CFO), the channel with high resolution and carries out data decoding. Based on a modularized point of view and motivated by the high resolution and low-complexity gridless VALSE algorithm, three modules named the VALSE module, the CFO estimation (CFO est.) module, and the decoder module are built. Soft information is exchanged between the modules to progressively improve the channel estimation and data decoding accuracy. Since the delays of multipath of the channel are treated as continuous parameters instead of on a grid, the leakage effect is avoided. Beside, the proposed approach is a more complete Bayesian approach as all the nuisance parameters, such as the noise variance, the parameters of the prior distribution of the channel, and the number of paths are automatically estimated. Numerical simulations and sea test data are utilized to demonstrate that the proposed approach performs better than the existing grid-based joint channel and data decoding approaches. Furthermore, it is also verified that joint processing, including CFO estimation, provides performance gain. [ABSTRACT FROM AUTHOR]

Wind speed and its evolution over time and space are important for many purposes, particularly for climate studies. However, direct measurement at the ocean surface is problematic, and remote prediction using satellite and radar systems rarely offers the desired resolution or accuracy, especially in polar waters. Being able to predict wind speed from underwater acoustic noise is, therefore, a key aspect to improving climate models and monitoring systems. To date, empirical equations using the noise spectrum at a given frequency, fitted to the available data, have been adopted. To leverage on the whole noise spectrum, only recently supervised machine-learning regression has been proposed. This article exploits the concurrent acquisition of underwater noise and anemometer measurements over a period of 16 months, at 10-min steps, to demonstrate the superiority of regression models based on supervised learning over empirical equations. It is also shown that depending on the type of compounding implemented, different tradeoffs are achieved between accuracy and temporal resolution. Considering all samples in the data set, including those in which rainfall and passing ships occurred, and separating by cross-validation the samples used for training from those used for testing, it is shown that a regressor based on the random forest technique, followed by the compounding of the predictions over an interval of 1 h, provides a mean absolute error of 0.62 m/s and a correlation coefficient of 0.96. In addition, the robustness with respect to the parameter setting and the performance obtained in performing prediction in chronological order are evaluated. [ABSTRACT FROM AUTHOR]

Achieving reliable communications with low complexity is challenging for underwater acoustic communications with moving transceivers, where the time-varying channels need to be estimated and tracked accurately and data detection needs to be performed with low complexity. In this article, with the use of a superimposed training (ST) scheme, we address this challenge by developing a low-complexity channel estimation and tracking algorithm, which is then integrated with low-complexity data detection in the frequency domain. ST is used to acquire improved channel-tracking capability. Based on belief propagation, we design a message-passing-based low-complexity bidirectional channel estimation (LCE-MP) algorithm, where all computational intensive parts are handled by the fast Fourier transform (FFT) algorithm, thereby achieving very efficient implementation with logarithmic complexity. Specifically, a message-passing-based fast information collection algorithm is presented to acquire “local” channel estimates, followed by the fusion of local channel estimates to achieve a “global” estimate of the channel. It is shown that the computational complexity per channel tap is only in a logarithmic level for the channel estimation and tracking. Moreover, the ST-scheme-based LCE-MP algorithm is combined with FFT-based data detection and decoding, which are performed in an iterative manner. The overall complexity of channel estimation and data detection is in a logarithmic level, and the system delivers excellent performance thanks to the joint processing. Field experiments were carried out in Jiaozhou Bay in 2019, and the experimental results verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Due to the combined effects of different acoustic propagation, reflection patterns in different time-scale, time-varying underwater acoustic (UWA) channel generally exhibits hybrid sparsity, i.e., contains not only rapidly time-varying elements, but also stationary or slowly time-varying ones. While the classic sparse reconstruction algorithms such as orthogonal matching pursuit (OMP) generally do not take it into account, the existence of static multipath will unavoidably hinder the tracking of rapid time variations. In this paper, a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach is proposed to sequentially explore rapidly time-varying sparsity in a two-step sequential manner. Specifically, the static components are firstly separated via simultaneous orthogonal matching pursuit (SOMP) among continuous measurements. Upon the residual error of the SOMP, the measurement-wise OMP is applied for estimating the remained dynamic components. As the variation of the SOMP residual error indicates how rapidly the remained dynamic multipath components vary, the observation length of the OMP is adjusted according to the gradient of residual error to adapt to rapidly time-varying components. Finally, both types of sparse components are summed up to obtain the whole channel response. Numerical simulations as well as experimental results from field UWA communication data show that the proposed algorithm exhibits better performance in exploiting rapidly time-varying sparsity than the state-of-the-art compressive methods do under UWA channel. [ABSTRACT FROM AUTHOR]

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security. [ABSTRACT FROM AUTHOR]

We investigate the peak age of information (PAoI) in underwater wireless sensor networks (UWSNs), where Internet of underwater things (IoUT) nodes transmit the latest packets to the sink node, which is in charge of adjusting the sleep-scheduling to match network demands. In order to reduce PAoI, we propose active queue management (AQM) policy of the IoUT node, beneficially compresses the packets having large waiting time. Moreover, we deduce the closed-form expressions of the average PAoI as well as the energy cost relying on probability generating function and matrix-geometric solutions. Numerical results verify that the IoUT node relying on the AQM policy has a lower PAoI and energy cost in comparison to those using non-AQM policy. [ABSTRACT FROM AUTHOR]

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other’s entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX’s parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100 percent, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols. [ABSTRACT FROM AUTHOR]

The accurate detection and quantification of submerged targets has been recognized as a key challenge in marine exploration, one that traditional census approaches cannot handle efficiently. Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. This allows to capture the structure of the reflecting signal from the moving target and to separate it from clutter reflections. We evaluate system performance both on synthetic (simulated) data, as well as on real data recorded over 50 sea experiments in a variety of sea conditions. When tested on real signals, the network trained on simulated patterns showed non-trivial detection capabilities, suggesting that transfer learning can be a viable approach in these scenarios, where tagged data is often lacking. However, training the network directly on the real reflections with data augmentation techniques allowed to reach a more favorable precision-recall trade-off, approaching an ideal detection bound. We also evaluate an alternative model based on recurrent neural networks which, despite exhibiting slightly inferior performance, could be applied in scenarios requiring on-line processing of the reflection sequence. [ABSTRACT FROM AUTHOR]

Underwater acoustic communication plays an essential role in underwater wireless information exchange, and nodes with such an ability constitute an underwater acoustic sensor network (UASN). However, UASNs face many challenges, such as high bit error rate and high propagation delay, significantly reducing network performance. Because of the challenging nature of underwater environments, the power consumption and battery energy efficiency of sensor nodes need to be considered. Directional communication technology can conserve energy by restraining beam directivity and diminishing interference between nodes by enabling peer-to-peer communication with intended neighbors. However, the technology suffers from the problems of neighbor discovery, deafness effect, and hidden/exposed terminal. To address these challenges, we propose a full-duplex directional medium access control (MAC) framework based on the vector hydrophone and multimodal transducer in this paper, along with a benchmark MAC protocol, which enables adaptive power control to achieve spatial reuse and energy conservation. Simulation results show that the benchmark MAC protocol outperforms existing representative protocols in terms of throughput, end-to-end delay, and energy efficiency. [ABSTRACT FROM AUTHOR]

Deep learning techniques have the potential to address real-time source localization and seabed characterization from passive ocean acoustic signals. The challenge grows when considering single-sensor cases that lack absolute travel time with no known source or environmental parameters. To address this, convolutional neural networks (CNNs) are trained to predict the source–receiver range and the seabed type on extracted and normalized 1-s pressure time series from explosive charges measured during the 2017 Seabed Characterization Experiment. To investigate the impact of ocean variability, two training data sets containing different sets of water sound-speed profiles (SSPs) are used. CNNs trained on the first synthetic data set, which contains a static ocean depth but significant variability in water SSPs, predict seabed class more accurately than the second data set from the measured data. Conversely, CNNs trained on the second synthetic data set, which contains fewer SSPs (similar to those measured during the experiment) and multiple ocean depths, predict the source–receiver range more accurately than the first data set from the measured data. The CNNs, with a degree of uncertainty, predict the seabed type and the source range when trained on synthetic data that have been stripped of source–receiver information, such as arrival time and amplitude. The CNNs trained to only predict the seabed type also make more accurate predictions on the seabed type when compared to those for multiple prediction types. This study highlights the importance of accounting for ocean variability and prediction uncertainties in training deep learning algorithms for marine applications. [ABSTRACT FROM AUTHOR]

Shallow water propagation can be described using modal theory. For low frequency sources, propagated signals are composed of a few dispersive modes, each of them propagating with its own frequency-dependent wavenumber. Modal estimation, and particularly wavenumber estimation, is of great interest in seabed characterization but classically requires a large and dense horizontal line array (HLA). The compressed sensing (CS) paradigm, which allows one to reduce the number of sensors, has been used to overcome this limitation. However, CS performance is directly linked to the discrete basis used in the process and is known to degrade with basis mismatch. To mitigate this issue, this article proposes a physics-based grid-free approach to perform wavenumber estimation using a HLA with a limited number of sensors and a single broadband source. The proposed method has three main features: it starts with a speed correction to prevent wavenumber aliasing (using water sound speed at the array location), it then embeds physical prior (the modal dispersion relation) at the core of the CS framework, and it involves a CS grid-free approach. The performance of the method is quantified on simulated data using the Jaccard’s distance. It has been applied successfully on experimental data from the 2017 Seabed Characterization Experiment. [ABSTRACT FROM AUTHOR]

This article presents geoacoustic inversion results for modal-dispersion data collected during the 2017 Seabed Characterization Experiment on the New England Mud Patch, an area where the seabed is characterized by an upper layer of mud. The experiment utilized a combustive sound source and a vertical line array of receivers at 5.4-km range. Using a careful combination of source deconvolution and warping time–frequency analysis, modal dispersion data (arrival time as a function of frequency) are estimated for 18 modes between modes 1 and 21. The modal dispersion data are then used to estimate seabed geoacoustic profiles and uncertainties via transdimensional Bayesian inversion. This article demonstrates the capacity to estimate high-order modes using warping. Comparing inversion results obtained with subsets of (lower order) modes to those obtained with the full set of available modes highlights the rich data information content carried by high-order modes. The results suggest a small sound-speed increase over the first 8 m of the seabed, the upper portion of the mud layer, which some earlier studies found to be isospeed. Overall, the inversion results are consistent with in situ measurements, as well as with previous geoacoustic inversion results. [ABSTRACT FROM AUTHOR]

This article proposes a transdimensional (trans-D) geoacoustic inversion method adapted to range-dependent (RD) propagation tracks based on prior information from a high-resolution seismic survey. Most trans-D inversions to date model the seabed as a stack of range-independent homogeneous layers, with unknown geoacoustic parameters and an unknown number of layers. The proposed method models the seabed as an unknown number of homogeneous sediment layers with an RD thickness structure and applies an adiabatic normal-mode model to predict acoustic propagation. To do so, the method extrapolates trans-D seabed models proposed at the receiver position over the range of the propagation track using reflector-interface information from a seismic survey. The method is applied successfully to modal time–frequency dispersion data collected over an RD track during the 2017 Seabed Characterization Experiment (SBCEX). [ABSTRACT FROM AUTHOR]

This article aims to derive the analytical expressions for the Doppler spectrum and Doppler shift at the receiver for the signal reflected from a moving undulating surface. The sea surface is represented using the sum of sinusoids and modeled using the Pierson–Moskovitz wave spectrum. The spectrum for each reflected path is observed to be a function of the slope of the sea surface around the scattering point and scatterer location. The derived expression for the Doppler shift is general and can be used to design an optimized system to enable Doppler compensation at the receiver. [ABSTRACT FROM AUTHOR]

Ocean fronts can cause the accumulation of nutrients and affect the propagation of underwater sound, so high-precision ocean front detection is of great significance to the marine fishery and national defense fields. However, the existing ocean front detection methods either have low detection accuracy or regard it as a binary classification problem to only detect where the ocean front occurs and cannot identify and distinguish various categories of ocean fronts with different behavior characteristics in different times and regions. In order to solve the above problems, we propose a semantic segmentation network called location and seasonality enhanced network (LSENet) for multiclass ocean fronts detection at pixel level. In this network, we first design a channel supervision unit (CSU) structure, which integrates the seasonal characteristics of the ocean front itself and the contextual information to improve the detection accuracy. We also introduce a location attention (LA) mechanism to adaptively assign attention weights to the fronts according to their frequently occurred sea area, which can further improve the accuracy of multiclass ocean front detection. Compared with other semantic segmentation methods and current representative ocean front detection method at Offshore China and Gulf of Mexico, the experimental results demonstrate convincingly that our method is more effective. The source code and datasets are released and publicly available via https://github.com/lliusha1155/LSENET. [ABSTRACT FROM AUTHOR]

A joint energy and correlation detection aided orthogonal frequency division multiplexing differential chaos shift keying (JECD-OFDM-DCSK) system is presented in this paper, where the reference and information-bearing signals of JECD-OFDM-DCSK are superimposed in the same time slot so that the phase offset between the reference and information-bearing signals is addressed. In JECD-OFDM-DCSK, only some subcarriers are activated and additional information bits are transmitted by the indices of these activated subcarriers, thereby improving the data rate while reducing the energy consumption. The energy efficiency, peak to average power ratio (PAPR) performance and system complexity of JECD-OFDM-DCSK are analyzed and then compared to other systems. The comparison results show JECD-OFDM-DCSK can obtain higher energy efficiency and better PAPR performance compared to its competitors at the cost of higher system complexity. Furthermore, the bit error rate (BER) expressions of JECD-OFDM-DCSK are derived in additive white Gaussian noise (AWGN) and multipath fading channels. Simulation results show that the BER performance of JECD-OFDM-DCSK outperforms that of other OFDM-based DCSK systems. Finally, JECD-OFDM-DCSK performs much better than other systems over the underwater acoustic (UWA) channel, which validates the robustness of JECD-OFDM-DCSK. [ABSTRACT FROM AUTHOR]

Energy-efficient and link-reliable underwater acoustic communication (UAC) systems are of vital importance to both marine scientific research and oceanic resource exploration. However, owing to the unique characteristics of marine environments, underwater acoustic (UWA) propagation experiences arguably the harshest wireless channels in nature. As a result, traditional model-based approaches to communication system design and implementation may no longer be effective or reliable for UAC systems. In this article, we resort to machine learning (ML) techniques to empower UAC with intelligence capabilities, which capitalize on the potential of ML in progressively improving system performance through task-oriented learning from data. We first briefly overview the literature of both UAC and ML. Then, we illustrate promising ML-based solutions for UAC by highlighting one specific niche application of adaptive modulation and coding (AMC). Lastly, we discuss other key open issues and research opportunities layer-by-layer, with focus on providing a concise taxonomy of ML algorithms relevant to UAC networks. [ABSTRACT FROM AUTHOR]

Security is a critical consideration in Underwater Acoustic Networks (UANs) due to the importance of the applications in which these types of networks are often employed, from military applications to marine natural disaster prevention. Furthermore, even simple Denial of Service (DoS) attacks such as jamming can be very effective in disrupting the communication, with significant negative consequences for these critical applications. While jamming has been widely studied in the context of terrestrial networks, the peculiarities of propagation in UANs, such as the low propagation speed, the multipath, and the high delay spread, need to be considered: the relative positions of the jammer, transmitter, and receiver can have a huge impact on the feasibility and impact of reactive jamming, opening the way for the exploitation of other jamming models. In this paper, we analyze the effectiveness of a reactive and a blind jammer through a game theoretical framework, comparing them for different geometries of the scenario. We assess the impact of the different jammers, employing different active and evasive strategies, where the first type of countermeasure implies the use of additional energy to protect the communication, while the second tries to avoid the jamming signals by randomizing the transmission pattern. [ABSTRACT FROM AUTHOR]

Strapdown inertial navigation system (SINS)/ ultra- short baseline (USBL) integrated navigation system is widely used in navigation and positioning of underwater vehicle. In an unknown and complex underwater environment, unstable USBL measurement information can lead to decreased navigation accuracy. In the traditional method, the interference to USBL is uniformly modeled as outliers, but in practice, it is also represented as slowly varying errors, update irregular errors. The latter two errors are not easy to be detected at the beginning of occurrence. Therefore, a SINS/USBL integrated navigation technology based on novel adaptive neural fuzzy inference system (ANFIS) is proposed. This model can detect and compensate abnormal USBL information and maximize USBL information. In order to improve the prediction accuracy of ANFIS, an ANFIS algorithm based on variational Bayesian Kalman filter and principal component analysis (PCA) is proposed to avoid the decrease of positioning accuracy caused by false compensation. The simulation shows that the designed ANFIS algorithm can accurately predict the above anomalies. And it can overcome the problem that USBL measurement anomalies are difficult to be detected at the initial stage. Combined with the proposed integrated navigation technology, it obtain the same positioning accuracy in complex environment as in good environment. The effectiveness and robustness of the proposed method are verified by river experiment. [ABSTRACT FROM AUTHOR]

Long propagation delay that causes throughput degradation of underwater acoustic networks (UWANs) is a critical issue in the medium access control (MAC) protocol design in UWANs. This paper develops a deep reinforcement learning (DRL) based MAC protocol for UWANs, referred to as delayed-reward deep-reinforcement learning multiple access (DR-DLMA), to maximize the network throughput by judiciously utilizing the available time slots resulted from propagation delays or not used by other nodes. In the DR-DLMA design, we first put forth a new DRL algorithm, termed as delayed-reward deep Q-network (DR-DQN). Then we formulate the multiple access problem in UWANs as a reinforcement learning (RL) problem by defining state, action, and reward in the parlance of RL, and thereby realizing the DR-DLMA protocol. In traditional DRL algorithms, e.g., the original DQN algorithm, the agent can get access to the “reward” from the environment immediately after taking an action. In contrast, in our design, the “reward” (i.e., the ACK packet) is only available after twice the one-way propagation delay after the agent takes an action (i.e., to transmit a data packet). The essence of DR-DQN is to incorporate the propagation delay into the DRL framework and modify the DRL algorithm accordingly. In addition, in order to reduce the cost of online training deep neural network (DNN), we provide a nimble training mechanism for DR-DQN. The optimal network throughputs in various cases are given as a benchmark. Simulation results show that our DR-DLMA protocol with nimble training mechanism can: (i) find the optimal transmission strategy when coexisting with other protocols in a heterogeneous environment; (ii) outperform state-of-the-art MAC protocols (e.g., slotted FAMA and DOTS) in a homogeneous environment; and (iii) greatly reduce energy consumption and run-time compared with DR-DLMA with traditional DNN training mechanism. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) have been widely promoted for developing various categories of marine applications, where the sensor nodes cooperate to complete specific tasks. Given the fact that the sensor nodes are unattended while continuously exposed to harsh environments, an associated trust model plays a significant role in node trustworthiness evaluation and defective node detection, such as the case of adverse attacks on the network. However, the existing trust models only evaluate the communication behavior and the energy of the sensor nodes, ignoring the effects of underwater environmental noise on trust reliability. Further, most trust models are designed with arbitraty weighted trust metrics, causing inevitable evaluation errors. To achieve the accurate calculation of node trust, we propose a new anomaly and attack resilient trust model, based on the isolation forest. We refer to this model as ITrust. The proposed ITrust model consists of two phases: trust metrics specifics and defective node detection. In the first phase, the trust dataset is integrated from four types of trust metrics: communication trust, data trust, energy trust, and environment trust. In the second stage, trust is evaluated with the obtained trust dataset using the isolation forest algorithm. Simulation results demonstrate that the proposed ITrust can detect defective nodes effectively, and achieves higher detection accuracy than that of the existing trust models in a noisy environment. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based modulation recognition methods are challenging in the case of few labeled samples and underwater impulsive noise. In this letter, we propose a novel network structure named IAFNet to achieve higher recognition accuracy of modulation signals with fewer samples in underwater impulsive noise environment. The IAFNet integrates impulsive noise preprocessing (INP), attention network (AN) and few-shot learning (FSL) to extract features more effectively through denoising and task-driven. Experimental results on simulation and practical data show that the IAFNet attains stronger anti-noise performance and better recognition performance on fewer labeled samples. Compared with other methods, the classification accuracy is improved by about 7%. [ABSTRACT FROM AUTHOR]

Cooperative transmission is a promising solution for reliable communication in underwater acoustic sensor networks. To fully exploit the benefits of cooperative system, efficient management of relay resources is required. However, the challenging working conditions, including imprecise channel information, limited observation feedback and dynamic underwater environment, call for an adaptive relay selection strategy for improving system capacity performance. In this paper, we present an innovative Partial Expert-based Reinforcement Adversarial Learning (PER-AL) strategy that unifies expert assisted learning and self-adjusting learning in a integral adversarial multi-armed bandit framework. The user constantly interacts with the environment and optimizes the relay decision by analyzing all gathered observation rewards, rather than relying on prior channel information. By introducing a novel expert learning mechanism into the initial learning stage, expert generator and expert learner are proposed to control the performance of experts and provide optimal expert advice to reinforce the initial relay learning for efficiency. With the deepening learning, the relay quality is then evaluated by analyzing the historical observable rewards directly through a Exp3 policy without engaging complexity too much. The proposed algorithm could automatically adapt to the underwater conditions and detect the optimal relay within a short time, significantly improving system capacity without jeopardizing the adversarial regret guarantee. Finally, extensive simulation results demonstrated the superior of PER-AL algorithm compared with recent existing CSI-based and CSI-free solution benchmarks. Particularly, PER-AL algorithm has a 28.3% improvement of the total network capacity and achieves a 44.5% decrease of implementation time. [ABSTRACT FROM AUTHOR]

This paper describes the design, characterization, and testing of a compact hydrophone capable of measuring acoustic signals from cardiomyocytes. Our hydrophone consists of a nanofabricated photonic-crystal diaphragm externally-mounted to the facet of an optical fiber to form a pressure-sensitive Fabry-Pérot cavity. Our hydrophone can operate in small liquid volumes less than 5 mm deep and incorporates a microchannel to vent air during immersion. The venting channel is designed to optimize bandwidth and sensitivity. Modeling and experimental results in water show a bandwidth from 50 Hz to 18 kHz and a minimum detectable pressure of $3~\mu Pa/ \sqrt {Hz}$ . We demonstrate the sensitivity to simulated bio-acoustic sources with nanometer-scale displacements.

Underwater sound in the oceans has been significantly rising in the past decades due to an increase in human activities, adversely affecting the marine environment. In order to assess and limit the impact of underwater noise, the European Commission's Marine Strategy Framework Directive (MSFD) included the long-term monitoring of low-frequency underwater sound as a relevant indicator to achieve a good environmental status. There is a wide range of commercial hydrophones and observing platforms able to perform such measurements. However, heterogeneity and lack of standardization in both hydrophones and observing platforms makes the integration and data management tasks time-consuming and error-prone. Moreover, their power and communications constraints need to be addressed to make them suitable for long-term ocean sound monitoring. Measured underwater sound levels are challenging to compare because different measurement methodologies are used, leading to a risk of misunderstandings and data misinterpretation. Furthermore, the exact methodology applied is not always public or accessible, significantly reducing ocean sound data re-usability. Within this work, a universal architecture for ocean sound measurement is presented, addressing hydrophone integration, real-time in situ processing and data management challenges. Emphasis is placed on generic and re-usable components, so it can be seamlessly replicated and deployed in new scenarios regardless of the underlying hardware and software constraints (hydrophone model, observing platform, operating system, etc.). Within the proposed architecture, a generic implementation of an underwater sound algorithm based on underwater noise measurement best practices is provided. Standardized and coherent metadata with emphasis on strong semantics is discussed, providing the building blocks for FAIR (Findable, Accessible, Interoperable, Reusable) ocean sound data management.

Underwater acoustic sensor networks (UASNs) have played a vital role in many security-sensitive applications like offshore oil exploration, tsunami forecast, and tactical surveillance. Due to the complex marine environment and harsh acoustic channel, UASNs face severe security challenges and attacks. Exploiting the multi-path energy from the richly scattering underwater environment, the time-reversal (TR) process can form the resonating strength based on the channel impulse response (CIR). Inspired by the natural link signature resulting from the spatial dependency of acoustic links, an authentication scheme using the maximum TR resonating strength is proposed, with the aim of effectively detecting the spoofing attacks in UASNs. To accommodate the time-varying nature of the underwater acoustic link, a database correlation method is exploited to capture the link CIR pattern over time for each link, which efficiently improves the accuracy of the authentication scheme using the TR process. Hence, the proposed algorithm enables each node to make the authentication decision based on maximum time-reversal resonating strength (MTRRS) locally with little overhead. It was evaluated by the probabilities of authentication, attack detection, and false alarm through extensive simulations. Finally, this MTRRS-based authentication was verified using the sea trial CIR data. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

The automatic modulation recognition (AMR) enables the receiver to automatically recognize the modulation type of the received signal for achieving correct demodulation. However, the noise and interference in the underwater acoustic channel greatly influence the features extracted from the signals. In this letter, we proposed the optimizing autoencoder (OAE) and the evaluation enhanced K-nearest neighbors (EEKNN) algorithms. The combination of OAE and EEKNN not only improves the feature discrimination but also avoids the misjudgment caused by abnormal samples and realizes accurate and efficient AMR. The experimental results of the data measured in the South China sea show that the proposed method successfully recognizes eight modulation types. The recognition accuracy is up to 99.25%, and the recognition time is only 3.48 ms. [ABSTRACT FROM AUTHOR]

Applications of underwater acoustics include sonar, communication, geophysical imaging, acoustical oceanography, and bioacoustics. Specialists typically work with little interdisciplinary interaction, and the terminology they employ has evolved separately in each discipline, to the point that transdisciplinary misunderstandings are common. Furthermore, increasing societal concern about possible detrimental effects of underwater noise on aquatic animals has led national and international regulators to require monitoring of underwater noise, with a consequent need for interdisciplinary harmonization of terminology. By adopting a common language, we facilitate the effective communication of concepts and information in underwater acoustics, whether for research, technology, or regulation. In the words of William H. Taft, “Don't write so that you can be understood, write so that you can't be misunderstood.” Clear definitions of widely used terms are needed, such as those used for the characterization of sound fields (e.g., “soundscape” and “ambient noise”), sound sources (“source level” and “source waveform”), sound propagation (“transmission loss” and “propagation loss”), and sound reception (“hearing threshold” and “frequency weighting function”). Terms that are used synonymously in one application have different meanings in another (examples include “hearing threshold” versus “detection threshold” and “transmission loss” versus “propagation loss”). Distinct definitions for these and many other acoustic terms are provided in a standard published in April 2017 by the International Organization for Standardization, ISO 18405. This article summarizes ISO 18405 and the process that led to the published definitions, including the reasons for omitting some terms. [ABSTRACT FROM AUTHOR]

Underwater noise analysis allows estimation of parameters of meteorological interest, difficult to monitor with in situ devices, especially in very harsh environments such as polar waters. Rainfall detection is a fundamental step of acoustical meteorology toward quantifying precipitation and, indirectly, wind. To date, this task has been conducted with some success by using a few frequency bins of the noise spectrum and combining their absolute values and slopes into some inequalities. Unfortunately, these algorithms do not perform well when applied to spectra obtained by averaging multiple noise recordings made over the course of an hour. Supervised, machine learning models allow the use of all the frequency bins in the spectrum, exploiting relationships that are difficult for a human observer to identify. Among the different models tested, a binary classifier based on random forest performed well with moderate computational load. Using a dataset consisting of over 18 000 hourly averaged spectra (approximately 25 months of in situ recordings) and comparing the results with measurements from a surface-mounted rain gauge, the proposed system detects precipitations greater than 1 mm/h with 90% probability, keeping the false alarm probability below 0.5%. This system has demonstrated remarkable robustness as performance is achieved without intentionally excluding any spectra corrupted by sounds produced by other sources, such as naval traffic and wind blowing over the sea surface. [ABSTRACT FROM AUTHOR]

Both source localization and environmental inversions are practical problems for long-standing applications in underwater acoustics. This paper presents an approach of the moving source localization and sound speed field (SSF) inversion in shallow water. The approach is formulated in a state-space model with a state equation for both the source parameters (e.g., source depth, range, and speed) and SSF parameters (first three empirical orthogonal function coefficients, EOFs) and a measurement equation that incorporates underwater acoustic information via a vertical line array (VLA). As a sequential processing algorithm that operates on nonlinear systems with non-Gaussian probability densities, an improved sequential importance resampling type particle filtering (SIR PF) is proposed to counter degeneracy. The improved PF performs tracking of source and SSF parameters simultaneously, and evaluates their uncertainties in the form of time-evolving posterior probability densities (PPDs). The performance of improved PF is illustrated with well-tracked simulations of real-time source localization and time-varying SSF inversion. Moreover, the influence of different particle numbers on PF tracking accuracy and computational cost is also demonstrated. Simulation results show that the high-particle-number PF has an outperform performance. For a given hardware system, the reasonable compromise between accuracy and computational cost is a matter of tradeoff.

Underwater acoustic sensor networks (UASNs) have gradually received attention due to their widespread applications, such as in disaster prevention, environmental monitoring and military activities, and they simultaneously face security challenges. In recent years, trust management mechanisms have become important tools for responding to internal attackers. However, most of the existing trust management mechanisms do not consider the adverse influence of complex underwater environment on node evaluation, and do not deal with the case that normal nodes are misjudged as malicious nodes. To achieve accurate trust evaluation of nodes and reduce the possibility of misjudgment of normal nodes, a redeemable Support Vector Machine-Dempster-Shafer (SVM-DS) fusion-based trust management mechanism (SDFTM) is proposed for UASNs in this paper. First, according to the characteristics of attacks, three kinds of trust evidence are selected: packet-based evidence, data-based evidence and energy-based evidence. Then the support vector machine (SVM) is applied to classify the trust of node from the aspect of each kind of trust evidence, while Dempster-Shafer (DS) evidence theory is used to fuse the different trust classification results of nodes. Second, to deal with cases where a normal node may be misclassified as a malicious node, trust redemption process is carried out based on the historical performance and environmental influence (unreliable acoustic channel, weak link connectivity). Finally, the trust value is calculated and updated. The simulation results prove that the proposed scheme yields satisfactory performance in malicious detection and reduce the possibility of misjudgment of normal nodes. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is a promising technique recognized for its merits in recovering sparse signals with enhanced resolution entailing specific constraints. In recasting the CS model within a Bayesian framework, its formulation can be interpreted and solved under various prior assumptions that may correspond to, e.g., the $\mathcal {L}_{1}$ or reweighted $\mathcal {L}_{1}$ constraint. Bayesian CS (or Bayesian sparse learning, BSL) achieves improved resolution and robustness compared with the deterministic CS. Therefore, BSL has been invoked to solve the single and multisnapshot beamforming models for direction-of-arrival (DOA) estimation. However, the recovery performance deteriorates for complicated signals because of nonsparsity. In this article, a multiconstraint BSL approach is proposed to solve the multisnapshot beamforming model (termed M-MCRBSL), which reconstructs the amplitude and DOA of the source simultaneously. With a proper assembly of constraints, the source can be represented sparsely and the transformation coefficients can be recovered accurately in multiple sparse domains. As demonstrated in the simulations, M-MCRBSL outperforms other state-of-the-art multisnapshot beamforming methods gauged by both the normalized mean square error (NMSE) and the structural similarity (SSIM) index. In particular, a deficiency sensitivity experiment is devised to elaborate the feasibility of the invoked invertibility approximation. As attested with an underwater acoustics experiment, M-MCRBSL with joint identity and Haar wavelet constraints achieves improved performance in terms of enhanced resolution and suppressed interference sources. [ABSTRACT FROM AUTHOR]

Acousticvector sensors have been widely used for direction-of-arrival (DOA) estimation in hydroacoustics and aeroacoustics. The orientation of the acoustic vector sensor must be measured, e.g., by an attitude sensor, before its application to DOA estimation. However, the measured orientation generally deviates slightly from its actual orientation because of imperfect attitude measurements. This letter investigates how random deviations in the orientation degrade the performance of DOA estimation. We derive a closed-form approximation of the hybrid Cramer-Rao bound for DOA estimation by modeling the deviations as zero-mean Gaussian variables with small variances. A relationship based on a determinant-differential formula is introduced to avoid a complicated brute-force solution, yielding a sufficiently simple expression with quantitative observations. Numerical results illustrate the high accuracy and effectiveness of the closed-form approximation. [ABSTRACT FROM AUTHOR]

Applying passive sonar to classify underwater acoustic targets at different depths is a challenging task. Although the self-contained hydrophone array can ensure the normal operation of most units in various environments, it is arduous to achieve precise time synchronization between each hydrophone, which results in difficulties in data fusion between hydrophones. For a vertical sonar array composed of self-contained units, a deep learning-based data compression and multihydrophone fusion (DCMF) model is proposed to quickly extract acoustic propagation interference features, which are used for underwater acoustic target classification. Unlike the frequency-range domain striation features acquired by long-term accumulation, this paper exploits the depth difference between multiple hydrophones to obtain the frequency-depth domain joint striation features in a short time. The proposed DCMF conducts efficient feature compression and fusion via parallel stacked sparse autoencoders and a multi-input fusion network. The experimental results illustrate that the compressed features have strong robustness, a low mean square error with the simulation results, and shorter signal length requirements, which improves the classification efficiency and real-time performance of DCMF. In the case of the experimental dataset, DCMF is compared with several state-of-the-art multiscale fusion models, and the experiments indicate that DCMF has the best performance and smallest computational complexity. [ABSTRACT FROM AUTHOR]

Acquiring accurate channel state information and mitigating severe intersymbol interference are challenging for underwater acoustic communications with moving transceivers due to the rapid changes of the underwater acoustic channels. In this work, we address the issue using a superimposed training (ST) scheme with a powerful channel estimation method. Different from the conventional time-multiplexed training, training sequences with a small power are superimposed with symbol sequences. The training signals are transmitted over all time, leading to enhanced tracking capability to deal with time-varying channels at the cost of only a small power loss. To realize this, based on the belief propagation, we develop a message-passing-based bidirectional channel estimation (BCE) algorithm, where all messages are Gaussian, enabling efficient implementation. In particular, the channel correlations are fully exploited through a forward recursion and a backward recursion, thereby achieving accurate channel estimation. Moreover, the ST-based BCE is combined with channel equalization (in the frequency domain) and decoding, and they are performed jointly in an iterative manner to significantly enhance the overall system performance. Field experiments were carried out in Jiaozhou Bay in 2019, and the results verify the effectiveness of the proposed scheme and algorithm. [ABSTRACT FROM AUTHOR]

With the extensive application of underwater acoustic sensor networks (UANs) in various fields such as commerce, marine environmental research, and national defense, the need for an autonomous and well-organized underwater acoustic network has been increasing. Topology discovery is a crucial step in constructing an underwater acoustic network, and node discovery and topology establishment are the essential components of the topology discovery process in UANs. This paper introduces the characteristics of underwater acoustic channels and networks and highlights their influences on topology discovery. We discuss the topology discovery protocol development in terrestrial networks (i.e., duty-cycle ad hoc network, Internet of things). The main focus of this paper is to study the topology discovery protocols of UANs. This paper also classifies and introduces the existing topology discovery protocols and compared their advantages and disadvantages to understand the current topology discovery methods. Furthermore, we also discuss the topology discovery protocol’s influence on different layers’ functions in the UAN protocol stack. Analyze the current research challenges in this field, followed by important open issues in UAN protocol development, which provide new opportunities for further research. [ABSTRACT FROM AUTHOR]

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models. [ABSTRACT FROM AUTHOR]

Underwater acoustic communication (UAC) modem is an important infrastructure of underwater network construction. In recent years, with the performance improvement of the STM32 processor, the realization of reliable UAC through high-performance STM32 processor is conducive to reduce the system power consumption, cheapen the hardware cost, and ease the development difficulty. In this letter, we propose an embedded UAC system with the STM32H743 processor as the core and the peripheral sending/receiving circuit as the signal conditioning circuit module. The system can support a variety of modulation and demodulation methods, including single/multi-carrier frequency-shift keying and orthogonal frequency division multiplexing. Furthermore, in order to reduce the computational cost of the system, a fast and robust frame synchronization algorithm based on the segmented fast Fourier transform is applied. The sea trials show that the system can realize the reliable UAC transmission of 100 b/s–1 kb/s in the distance of 5–8 km in the shallow water area. [ABSTRACT FROM AUTHOR]