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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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

Sonar performance prediction models require environmental inputs, including bottom depth and composition, sound-speed profile (SSP), and wave or wind conditions. Temporal and spatial variation and measurement uncertainties in environmental inputs result in uncertain model outputs. Ideally, acoustic fluctuations could be predicted from input parameter variability using a suitable modeling framework. A data set consisting of 460 SSPs and acoustic transmission loss (TL) measurements between 300 and 900 Hz was acquired during GLISTEN15, a 2015 Mediterranean Sea trial led by North Atlantic Treaty Organization's Centre for Maritime Research and Experimentation. The TL was measured in 5-min sampling periods at fixed source-to-receiver ranges of 0.5–10 km over two separate days. A gradual change in TL as large as 15 dB over 5 min was observed at some receivers and frequencies. Predicted TL variability was compared to measured TL variability by using the SSPs as inputs to an acoustic propagation model. Purely stochastic modeling either over- or underpredicted variability, depending on the temporal and spatial separation of the SSPs used as inputs. In this article, an internal wave model derived from the SSP time series was used to demonstrate the possible effect of internal waves propagating perpendicular to the source–receiver direction. The best agreement between modeled and measured variability was obtained for internal waves of 1536-s period and 0.8–1.6-m amplitude. In this way, an approach that included a physically plausible model of internal wave dynamics grounded in a few relevant in situ measurements provided good predictions of observed TL variability and a reasonable explanation of its origin. [ABSTRACT FROM AUTHOR]

The study of sound scattering from solid targets near a water–sediment interface is a topic of recent interest. Keeping in line with this direction of study, the following is an investigation of the various acoustic scattering mechanisms from a solid aluminum cylinder in broadside geometry near a flat air–water interface. In addition to identifying the possible scattering interactions that can occur, contained in this article is the explanation of the various methods utilized to investigate the different physical processes. Furthermore, the following treatment will utilize the geometrical optics approximation to treat targets as point particles to perform a simple analysis of the processes in the time domain. Although this approach is useful, it will not completely describe all aspects of the interactions but will provide a cursory understanding of the timing relations of the different identified mechanisms and will give an explanation of acoustic feature loci of a variety of possible scattering mechanisms. [ABSTRACT FROM AUTHOR]

For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time–frequency structures. The signal features in the form of a time–frequency map are fed into a deep convolutional neural network, referred to as a time–frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time–frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set. [ABSTRACT FROM AUTHOR]

The impact of bubbles on underwater acoustic communication has mostly been studied in environments with strong winds (> 12 m/s), and it has been found that the main impact is the strong attenuation due to dense bubble clouds. Without continuous replenishment during high winds, these dense bubble clouds dissipate rapidly in a few minutes, along with the strong attenuation. We find that after the dense bubble clouds dissipate, they leave behind a diffused bubble cloud with very small bubbles. The lifetime of this diffused bubble cloud is much longer than that of the dense bubble cloud. The attenuation due to these residual bubbles is small, but they result in an increase in channel variability due to their random motion. Furthermore, because they can be transported by currents to distant locations, we argue that these bubbles have a more persistent impact in many environments, including ones where winds are not very strong, but there are other sources of bubbles (e.g., shipping channels). We give a statistical characterization of the propagation through these bubbles, and show the impact on acoustic communications with experimental data. [ABSTRACT FROM AUTHOR]

As the mining of deep-sea natural deposits is becoming cost competitive compared to similar land-based mining, companies have started to dig into the seabeds to collect minerals. However, the acoustic contribution of this activity in the surrounding environment can be significant. To predict the impact of such noise, the starting point is to localize and quantify the sources that create it. In this study, a 3-D prototype acoustic array to perform this localization and quantification is designed, built, and deployed at sea for validation of its localization capacities. The design method performs a two-step study to define the array shape and select the hydrophone arrangement over it, under harsh constraints. Each step relies on two metrics to rank the candidates: the maximum sidelobe level, and the spatial resolution. These are computed on conventional beamforming maps for simulated sources that represent excavation machines on the ground. The shape is first determined to be the one that yields steady maximum sidelobe value levels over frequency. Second, the hydrophone arrangement that achieves the lowest maximum sidelobe level while limiting the spatial resolution is selected. This leads to a tip down conical array with 21 hydrophones, of about 3 m in height and diameter, and this is manufactured and used during an experimental campaign in the Mediterranean Sea. The experimental localization maps show strong agreement between the estimated source position and its ground truth. A more detailed comparison between simulated and real performances confirms accurate array conception and realization. Thus, this design procedure provides an efficient underwater acoustic array for monitoring deep-sea mining, the localization capacities of which are validated in a real-life setting. [ABSTRACT FROM AUTHOR]

Multiple features can be extracted from time–frequency representation of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time–frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals and large sequences of harmonics and pulsed bursts from common whales. [ABSTRACT FROM AUTHOR]

Reliable acoustic communication between submersibles and surface vessels plays a critical role in deep-sea exploration. Adaptive turbo equalization can effectively combat the selective fading of underwater acoustic channels, thereby becoming one of the enabling technologies for single-carrier deep-sea vertical acoustic communications. Existing adaptive turbo equalizer designs are usually based on a minimum-mean-squared-error criterion or a minimum-mean-absolute-error criterion. These criteria are inherently suboptimal with respect to the achievable symbol error rate (SER). In this article, an improved proportionate normalized minimum-SER (IPNMSER) algorithm is proposed for adaptive turbo equalization in deep-sea vertical acoustic communications. The proposed algorithm utilizes the minimum-SER (MSER) criterion to derive the equalizer update equations, aiming to minimize the system's SER directly. In addition, because the deep-sea vertical channel has a sparse structure, which leads to a sparse equalizer, a sparsity-aware proportionate-type approach is therefore incorporated into the framework of the MSER criterion to achieve faster convergence. To investigate the effectiveness of the proposed algorithm, we conducted a deep-sea vertical acoustic-communication experiment in the Challenger Deep of the Mariana Trench. The results demonstrated that the proposed IPNMSER algorithm can outperform a conventional normalized MSER algorithm and other well-known proportionate-type algorithms, achieving error-free detection for all data blocks over a vertical communication range of approximately 10500 m. [ABSTRACT FROM AUTHOR]

Multiple-input–multiple-output (MIMO) system offers a promising way for high data rate communication over bandwidth-limited underwater acoustic channels. However, MIMO communication not only suffers from intersymbol interference, but also introduces the additional co-channel interference, which brings challenge for underwater acoustic MIMO channel estimation and for channel equalization. In this article, we propose novel interference cancellation (IC) methods for handling this co-channel interference problem in the design of both channel estimation and channel equalization. Our method for channel estimation utilizes the spatial joint sparsity and the temporal joint sparsity in the multipath structure to estimate sparse channels with common delays under distributed compressed sensing framework. In this way, we enhance channel estimates with common delays, thus, suppress co-channel interference. Meanwhile, to address the case of multipath arrivals with different delays, which are estimated as noise under simultaneous orthogonal matching pursuit (SOMP) algorithm, we introduce forward–reverse strategy to SOMP algorithm, which is referred to as the FRSOMP algorithm. Our proposed FRSOMP algorithm performs the SOMP algorithm to achieve the initial channel estimates, performs the forward-add process, which attempts to add promising candidates into support sets, and performs the reverse-fetch process to check if the candidates in the support set are retained or removed. The purpose of channel estimation is to directly calculate the filter coefficients for channel-estimation-based decision feedback equalization (CE-DFE). In this article, we also propose a novel CE-DFE receiver with IC component. We design IC filters based on the traditional CE-DFE, and we derive the coefficients of the feedforward filters, feedback filters, and IC filters based on the channel estimate metric obtained by the FRSOMP algorithm, so the co-channel interference will be suppressed both in channel estimation and channel equalization. We demonstrate the performance of our approach by numerical simulation, lake experiment, and sea experiment. Results are provided to demonstrate the effectiveness of the proposed methods, which show that the proposed methods obtain higher output signal-to-noise ratio, lower bit error rate, and more separated constellations compared with the traditional compressed sensing channel estimation method and the traditional CE-DFE method. [ABSTRACT FROM AUTHOR]

This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation. [ABSTRACT FROM AUTHOR]

Over the past 25 years, there has been significant research activity in development and application of methods for inverting acoustical field data to estimate parameters of geoacoustic models of the ocean bottom. Although the performance of various geoacoustic inversion methods has been benchmarked on simulated data, their performance with experimental data remains an open question. This article constitutes the first attempt of an experimental benchmark of geoacoustic inversion methods. To do so, the article focuses on data from experiments carried out at a common site during the Shallow Water 2006 (SW06) experiment. The contribution of the article is twofold. First, the article provides an overview of experimental inversion methods and results obtained with SW06 data. Second, the article proposes and uses quantitative metrics to assess the experimental performance of inversion methods. From a sonar performance point of view, the benchmark shows that no particular geoacoustic inversion method is definitely better than any other of the ones that were tested. All the inversion methods generated adequate sound-speed profiles, but only a few methods estimated attenuation and density. Also, acoustical field prediction performance drastically reduces with range for all geoacoustic models, and this performance loss dominates over intermodel variability. Overall, the benchmark covers the two main objectives of geoacoustic inversion: obtaining geophysical information about the seabed, and/or predicting acoustic propagation in a given area. [ABSTRACT FROM AUTHOR]

The sparse direct adaptive equalization technique recently received many attentions for single-carrier underwater acoustic communications. By taking advantage of the sparse (nonuniform) structure of a direct adaptive equalizer (DAE), one obtains a sparse DAE with improved performance and/or reduced complexity compared with its nonsparse counterpart. In this article, the sparse DAE is revisited with two contributions made: First, a comprehensive comparison is made for existing sparse DAEs designed with the proportionate-updating (PU) or the zero-attracting (ZA) adaptive filtering principles. The comparison concludes that the PU-type DAE outperforms the ZA-type DAE in terms of complexity, performance, and operability, thus shall be favored in practical use. Moreover, it motivates the design of a sparse DAE, named the selective ZA improved proportionate normalized least mean squares DAE (SZA-IPNLMS-DAE), based on the combination of the PU and ZA principles. The SZA-IPNLMS-DAE outperforms existing sparse DAEs armed with only one sparsity-promoting mechanism; second, a partial tap update (PTU) scheme via hard thresholding is introduced to sparse DAEs for reducing their complexity without sacrificing performance. The resulting low-complexity and high-performance sparse PTU-DAE schemes are strong candidates for single-carrier UWA communications. Experimental results of multiple-input multiple-output UWA communications are presented to corroborate all above claims. [ABSTRACT FROM AUTHOR]

Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramér–Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel underwater acoustic (UWA) communication scheme that achieves energy and spectrum efficiency simultaneously by combining Doppler-resilient orthogonal signal division multiplexing (D-OSDM) and multiple-input–multiple-output (MIMO) signaling. We present both the transmitter processing and the receiver processing for MIMO D-OSDM. We evaluate the performance of MIMO D-OSDM in simulations with a large intersymbol interference of 25 symbols and a Doppler spread with a maximum Doppler shift of 8 Hz. In addition, the sea trial is performed in Suruga Bay, where the receiver is mounted on a barge and a research vessel with the transmitter makes round trips along a line with a speed of 4 kn. In the experiments, we obtain an intersymbol interference of 3.6–29.7 symbols and a Doppler spread of several Hertz (leading to a spread of over two to three subcarrier spacings). The simulation results suggest that MIMO D-OSDM has an advantage over normal D-OSDM, Doppler-resilient MIMO orthogonal frequency division multiplexing (MIMO D-OFDM), and classical OFDM with MIMO signaling (MIMO OFDM)—MIMO D-OSDM achieves better bit error rate performance than the benchmarks. The sea trial results also support the advantage of MIMO D-OSDM—it achieves a coded block error rate of 3.2% while normal D-OSDM and MIMO D-OFDM achieve a coded block error rate of 9.7% and 9.3%, respectively. We conclude that MIMO D-OSDM can become a viable technique that achieves reliable and effective UWA communication. [ABSTRACT FROM AUTHOR]

The research presented in this paper is aimed at developing a control algorithm for an autonomous surface system carrying a two-sensor array consisting of two acoustic receivers, capable of measuring the time-difference-of-arrival (TDOA) of a quasiperiodic underwater acoustic signal and utilizing this value to steer the system toward the acoustic source in the horizontal plane. Stability properties of the proposed algorithm are analyzed using the Lie bracket approximation technique. Furthermore, simulation results are presented, where particular attention is given to the relationship between the time difference of arrival measurement noise and the sensor baseline—the distance between the two acoustic receivers. Also, the influence of a constant disturbance caused by sea currents is considered. Finally, experimental results in which the algorithm was deployed on two autonomous surface vehicles, each equipped with a single acoustic receiver, are presented. The algorithm successfully steers the vehicle formation toward the acoustic source, despite the measurement noise and intermittent measurements, thus showing the feasibility of the proposed algorithm in real-life conditions. [ABSTRACT FROM AUTHOR]

The technique of underwater acoustic localization (UAL) is widely used to track submerged targets. In the present study, we consider UAL in the specific application of locating the Mediterranean slipper lobster, Scyllarides latus (S. latus), for the aim of continuous and long-term investigation of its movement patterns. Since visual inspections only offer a snapshot to the life of these nocturnal crustaceans, we used underwater acoustic tags and deployed the first testbed to track a community of S. latus through UAL. The testbed included 19 acoustically tagged S. latus, as well as a sensor network of four receivers that record the time-of-arrival of detected tag's emissions. The testbed operated for six months, and the data collected were analyzed offline. In this paper, we describe the design details of our testbed. We discuss the considerations in choosing both the type of acoustic tags and the structure of the testbed. We also present our algorithm to time synchronize the receivers, and the localization procedure. Our preliminary results show that, for this long-term deployment, the widely used model of clock offset for the receivers is too simplified, and that time synchronization must consider also the clock's skew. We also show that the tagged lobsters are mostly detected by fewer than three receivers, thereby making localization using traditional trilateration methods highly challenging. Finally, preliminary results for the localization of the tagged lobsters in the deployment area are presented. [ABSTRACT FROM AUTHOR]

Small explosions were used as sound sources in the Seabed Characterization Experiment conducted in spring 2017 in the New England Mud Patch, a shallow-water region with a depth of approximately 75 m. The sources were U.S. Navy Signal Underwater Sound (SUS) Mk 64 charges, which contained 31.18 g of the explosive 2,4,6-Trinitrophenylmethylnitramine, commonly referred to as Tetryl. Source recordings were obtained by two hydrophones deployed from the same ship that deployed the SUS. The recordings were analyzed for bubble period, energy spectral density, and the variability of these parameters, and compared to previous results from the literature, including the prediction of a historic spectral model, and a new semiempirical time-domain model assembled using measured data from the literature. The new model describes the source level measurements in the 25–275-Hz band and in the 400-Hz octave band to within 0.5 dB, and agrees with similar measurements from the literature to within 0.6 dB. The standard deviation of the band-limited source levels was found to be about 1 dB, some of which is ascribed to uncertainty and variation in the source-to-receiver distance. The observed source level variation is similar to previously reported values. [ABSTRACT FROM AUTHOR]

The autonomous passive-acoustic lander Deep Sound was deployed at five locations during the Office of Naval Research (ONR)-supported Seabed Characterization Experiment, a multi-institutional field effort held at the New England Mud Patch, where the seabed is known to consist of a thick layer of silt and clay overlying a medium and coarse sand. The five deployments of Deep Sound were up to 9 h long, during which time ambient noise data, taken over an acoustic bandwidth of 5 Hz–30 kHz, were collected on four hydrophones arranged in an inverted “T” shape. Local temperature and conductivity were also recorded continuously at each location. A wave number integral model of wind-driven noise in a fluid waveguide over a two-layered elastic seabed was used to calculate the dependence of the vertical noise coherence on the geoacoustic properties in the overlying silt and clay layer, as well as the subbottom sand half-space. The modeled noise coherence was fitted to the data over the band 100 Hz–12 kHz, returning the compressional- and shear-wave speeds and densities of both layers and the thickness of the top layer. [ABSTRACT FROM AUTHOR]

The Intensity Vector Autonomous Recorder (IVAR) is a system that records four coherent channels of acoustic data continuously: one channel for acoustic pressure and three channels associated with a triaxial accelerometer from which acoustic particle velocity is obtained. IVAR recorded the vector acoustic field in broadband signals originating from Signal, Underwater Sound (SUS) (Mk-64) charges deployed at 5–13-km range from the fixed IVAR site (mean depth 74.4 m) as part of the 2017 Seabed Characterization Experiment (SBCEX) designed to study the acoustics of fine-grained muddy sediments. Sufficient geometric dispersion at these ranges permitted unambiguous identification of up to four modes as a function of frequency for frequencies less than 80 Hz. From time–frequency analysis of the dispersed arrivals, a single mode ($n$) and single-frequency ($f_{i})$ properties are identified at peaks in the narrowband scalar field, with time dependence corresponding to mode group speed. At these time–frequency addresses, four quantities derived from the vector acoustic measurements are formed by coherent combination of pressure and velocity channels: first, modal phase speed; second, circularity, a measure of the normalized curl of active intensity; third, depth-dependent mode speed of energy; and fourth, vertical component of reactive intensity normalized by scalar intensity. A means to compute these quantities theoretically is provided, and a comparison of model results based on a notional geoacoustic representation for the SBCEX experimental area consisting of a single low-speed mud layer over a half-space area versus a Pekeris representation based on the same half-space shows a striking difference, with the field observations also clearly at variance with the Pekeris representation. A fundamental property of mode 2, observed at the IVAR location, is a change in sign for circularity and vertical reactive intensity near 37 Hz that is posited as a constraint observation for mode 2 that must be exhibited by any geoacoustic model that includes a low-speed mudlike layer applied to this location. [ABSTRACT FROM AUTHOR]

This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content. [ABSTRACT FROM AUTHOR]

Very-high-frequency (VHF) underwater acoustic communication is a relatively unexplored terrain, but its importance is expected to increase with the growing demands for larger bandwidths and higher data rates. Knowledge of VHF propagation channels is a prerequisite to achieve high data throughput. In this paper, we present time-variant angle-resolved impulse responses for a set of 250-kHz shallow-water channels. The measurements were performed at high resolution using 64 hydrophones arranged in a line array. The measured channels are characterized by delay spreads of several milliseconds, Doppler spreads of tens of hertz, and angular spreads up to $30 ^\circ$. The angular spread is not separable from the delay-Doppler spread, and the impulse response depends heavily on the direction of arrival. Different beamforming and multichannel equalizer strategies are compared for a single-input–multiple-output configuration. A method combining subarray beamforming and multichannel equalization delivers in both noise- and interference-limited scenarios, transferring data at 78.125 kb/s over ranges up to 770 m. [ABSTRACT FROM AUTHOR]

Achieving high throughput and reliability in underwater acoustic networks for transmitting distributed and large volume of data is a challenging task due to the bandwidth-limited and unpredictable nature of the acoustic channel. In a multinode network, such as in the Internet of Underwater Things, communication link efficiency varies dynamically: if the channel is not in good condition, e.g., when in deep fade, channel coding techniques may fail to deliver the information even with multiple retransmissions. Hence, an efficient and agile collaborative strategy is required to allocate appropriate resources to the communication links based on their statuses. The proposed solution adjusts the physical- and link-layer parameters collaboratively for a code-division multiple-access (CDMA)-based underwater network. An adaptive hybrid automatic repeat request solution is employed to guarantee reliable communications against errors in poor links. Results were validated using data collected from the LOON testbed—hosted at the North Atlantic Treaty Organization Science and Technology Organisation Centre for Maritime Research and Experimentation in La Spezia, Italy—and from the REP18-Atlantic sea trial conducted in September 2018 in Portuguese water. [ABSTRACT FROM AUTHOR]

Field data are often expensive to collect, time-consuming to prepare to collect, and even more time-consuming to process after the experiment has concluded. However, it is often the practice that such data are used for little after the funded research activity that was concomitant with the experiment is completed. Immutability of the original experimental configuration either results in regathering of expensive field data, or in absence of such data, model-dependent analysis that partially captures the real-world dynamics. For underwater acoustic research and development, the standard communication pipeline might be modified to enable greater reusability of experimental field data. This paper first characterizes the necessary modifications to the standard communication pipeline to prepare signals for transmission and subsequent recording such that research trades for different modulation and coding schemes may be undertaken postexperiment, without the need for retransmission of additional waveforms. Then, using the modified mathematical framework, sufficient conditions for reliable postexperimental replay of the environment are recognized. Finally, techniques are discussed to collect sufficient environmental statistics such that subsequent research can be accomplished long after the experiment has been completed, and that results from a given experiment may be reasonably compared with those of another. Examples are provided using both synthetic and experimental data collected from at-sea field tests. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an automatic repeat request (ARQ) for reliable transmission over half-duplex links. We design a method based on grouped packet coding (GPC) that combines a stop-and-wait (S&W) ARQ procedure with random linear packet coding and selective acknowledgments applied to groups of coded packets. Our goal in doing so is to boost the throughput efficiency on poor-quality links with long delay. Such links are notably encountered in underwater acoustic channels, where the bit error rate may be as high as $10^{-3}$ and round-trip delays can be measured in thousands of bits. To quantify the benefits of the proposed S&W-GPC method, we evaluate its throughput efficiency analytically and compare it with the throughput efficiency of standard S&W methods, as well as the benchmark efficiency of full-duplex methods. Our results show that S&W-GPC outperforms all other techniques on half-duplex links with long delay, as well as rateless packet coding on full-duplex links with long delay. We present results for a point-to-point link, as well as for a multicast network. In addition to the performance analysis, we offer guidelines for an optimal system design, which involves a judicious choice of the packet size, packet coding, and grouping parameters. [ABSTRACT FROM AUTHOR]

We calibrated a sound velocimeter to a precision of +/- 0.034 m/s using Del Grosso's sound-speed equation for seawater at temperatures of 2, 7.2, 11.7, and 18 degrees C in a tank of seawater of salinity 33.95 at one atmosphere. The sound velocimeter measures the time-of-flight of a 4-MHz acoustic pulse over a 20-cm path by adjusting the carrier frequency within a 70-kHz band until the pulse and its echo are inphase. We used the adjustable carrier frequency to determine the internal timing characteristics of the sound velocimeter to nanosecond precision. Similarly, sound-speed measurements at four different temperatures determined the acoustic pathlength to micrometer precision. The velocimeter was deployed in the ocean from the surface to 4500 dbar alongside conductivity, temperature, and pressure sensors (CTD). We demonstrated agreement of +/- 0.05 m/s (three parts in 10(5))-with CTD-derived sound speed using Del Grosso's seawater equation from 500 to 4500 dbar after removing a bias and a trend.

JASCO's Airgun Array Source Model (AASM) is a combined deterministic and stochastic model that separately treats the low-frequency and high-frequency components of signals produced by airgun arrays. The low-frequency module is based on solving the equations of motion for interacting spherical bubbles. The high-frequency module is based on a stochastic model of the airgun spectrum, which has been derived from a principal component regression analysis of the experimental data. This stochastic model determines the frequency spectrum of an airgun waveform during the rapid onset of a pressure that occurs when air is released from the gun chamber. AASM combines the output of these two modules to predict the source waveform of an airgun array over a wide frequency range (0–25 kHz). AASM was among the source models included in benchmark comparisons presented at the International Airgun Modeling Workshop (IAMW), held in Dublin, Ireland, in 2016. Results from the workshop showed that different source models agreed reasonably well at low frequencies (<200 Hz), but they diverged substantially at high frequencies (>1 kHz). To help better understand the reasons for a mismatch between source models, this paper presents solutions to the IAMW source test cases, calculated using AASM, as well as a detailed description of AASM's theoretical underpinnings. [ABSTRACT FROM AUTHOR]

This paper describes the theoretical and numerical basis for the Centre for Marine Science and Technology airgun array model (Cagam), a relatively simple airgun array model that has been used by the center for a number of years. Comparisons with the measured data from the International Association of Oil and Gas Producers Exploration and Production Sound and Marine Life, Joint Industry Programme Svein Vaage Broadband Airgun Study data set, were carried out for a single airgun of a range of sizes and types and several two-gun clusters. When calibrated for the time integrated squared pressure, the model predicted positive peak levels to an accuracy of ±0.6 dB and successfully predicted the overall spectral shape, including its high-frequency roll-off. There were larger errors in prediction of negative peak pressures, with Cagam underpredicting the negative peak pressure by up to 2.3 dB. For a single airgun, Cagam tended to slightly overestimate the bubble pulse frequency and to underestimate the bubble damping, with both these discrepancies increasing for two-gun clusters. A comparison with the Agora2 model, used for several studies by participants in the 2016 International Airgun Modelling Workshop, is also included. [ABSTRACT FROM AUTHOR]

This paper aimed at comparing different propagation models for the calculation of sound pressure fields in the ocean as well as sound exposure levels. The chosen models are freely available and commonly used by the general underwater acoustics community when sound propagation losses need to be predicted in an ocean environment. The models used were Ocean Acoustics and Seismic Exploration Synthesis (OASES), RAM, Bellhop, and Kraken, which are all based on different approximations of the wave equation. The comparison exercise was carried out by calculating propagation loss, sound pressure waveforms, and sound exposure levels produced by a modeled air gun source. Calculations were performed for a simple range-independent problem of a Pekeris waveguide and produced insights into applicability of each model with regards to near- and far-fields, as well as low and high frequencies for example. Two of the models, RAM and Bellhop, which are suitable to range-dependent problems, were also used to solve a slightly complex case having a sloping bottom. It is hoped that the results presented here will be particularly useful to those who need to use propagation models for marine environmental and bioacoustics studies. [ABSTRACT FROM AUTHOR]

Scientific questions regarding the impact of anthropomorphic noise in the marine environment have resulted in an increasing number of regulatory requirements and precautionary mitigation strategies to reduce the risks associated with high-resolution marine geophysical surveys performed in waters subjected to government jurisdiction. An example of regulatory frameworks includes the Marine Mammal Protection Act in the United States and the Marine Strategy Framework Directive 2008/56/EC in the European Union. Regulatory compliance often requires an assessment of the potential ecological risks before initiating a marine geophysical survey. However, the acoustic source data needed to estimate the risk associated with the operation of a given high-resolution survey system are frequently lacking. A comprehensive measurement program was performed to quantify the characteristics of sounds radiated by a variety of commercial marine geophysical survey systems, including boomers, sparkers, airguns, chirp sub-bottom profilers, sidescan sonars, and swath-bathymetric sonars [Crocker and Fratantonio, “Characteristics of high-frequency sounds emitted during high-resolution marine geophysical surveys,” Naval Undersea Warfare Center, Newport, RI, USA, NUWC-NPT Tech. Rep. 12, 203, 2016]. Calibrated acoustic source data, including source levels, source spectra, and beam patterns, were acquired for a total of 18 different marine geophysical survey systems. The data support modeling to estimate the potential ecological impacts resulting from the operation of certain high-resolution marine geophysical survey systems. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]

Acoustic propagation in shallow water at low frequency is characterized by a few propagating modes. When the source is impulsive or short enough, the modes can be extracted from the signal received on a single sensor using a warping operator. This opens the door to single-receiver matched-mode processing (SR-MMP) as a means to estimate source location and/or ocean environmental parameters. While the applicability of SR-MMP has been demonstrated through several experiments, prediction of its achievable performance has not been fully investigated. In this paper, performance analysis of SR-MMP is carried out using numerical simulations of a typical shallow water environment, incorporating possible environmental mismatch as well as degradations resulting from nonideal modal filtering. SR-MMP is a nonlinear estimation problem that presents three regions of operation: the high SNR asymptotic region driven by local errors, the intermediate SNR threshold region driven by sidelobe ambiguities and the low SNR no-information region. The method of interval errors, which gives computationally efficient and reliable mean squared error performance prediction, is used to conduct the analysis. The results suggest that the SR-MMP performance depends strongly on the source/receiver depth. A significant loss in performance is observed when the receiver is located at a node common to two modes. Receiver depth must therefore be chosen with care. SR-MMP seems to be quite robust to mismatch on the seabed properties alone but does not handle well the combined effect of seabed and water column mismatches. Nonideal modal filtering has a moderate impact on performance. [ABSTRACT FROM AUTHOR]

Bidirectional underwater acoustic communications usually request the transmission of some control information among nodes to govern the data flow. Control signaling, employed as frame reception validation, is also a peculiarity of the Automatic Repeat on reQuest (ARQ) schemes. An alternative solution to the error control problem is using forward error correction, even though high redundancy is needed to obtain the low error rate, hence leading to a sensible throughput reduction. Furthermore, no control on data integrity is allowed. For the above reasons, we explore the possibility of implementing three different ARQ schemes by considering only the use of an explicit negative acknowledgment (NACK) for requiring frames retransmission. Specifically, the NACK frame index is related to a frequency-dependent message and the information frame(s) to be retransmitted are recognized, owing to a frequency-domain detection mechanism. Moreover, the proposed solution is very efficient when applied in a broadcast environment since it overcomes the necessity of performing a multiuser detection at the centralized broadcaster. In this context, we report both simulations and experimental tests results to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Preamble detection before data transmission is an important module for an underwater communication system, since false detections would bring undesirable consequences in terms of the lifetime and coexistence of underwater networks. This paper conducts a thorough investigation of preamble detection in adverse underwater acoustic channels in the presence of various external interference, such as narrowband interference, impulsive noise, partial-band partial-block-duration interference, and chirps from nearby systems—The Gaussian noise model is not sufficient. We propose two novel detection methods based on the inherent sparsity of underwater acoustic channels: The first method declares signal detection when a sparse signal reconstruction is successful, whereas the second method accumulates the correlation coefficients from multiple paths instead of just the strongest path. We study the performance of the proposed methods based on simulations and experimental data sets, where the detection thresholds are empirically obtained. Performance results testify to the clear advantages of the proposed detection methods relative to existing matched-filter based approaches in various interference conditions. [ABSTRACT FROM AUTHOR]

This paper develops a linearized Bayesian inversion algorithm for high-precision localization of an autonomous underwater vehicle (AUV) on a test range using time difference of arrival acoustic data. The Bayesian approach allows the uncertainties of several factors, including precise receiver locations and bias and lateral variability of the water-column sound-speed profile, to be included in the inversion as unknown parameters with varying levels of prior information. This provides better estimates of these parameters (and hence AUV locations), and includes the effect of their uncertainties in the uncertainty estimates for AUV locations. A modeling study comparing AUV localization uncertainties from the linearized inversion that results from nonlinear Monte Carlo analysis shows that linearization errors are small, and hence linearized analysis is used to efficiently map AUV localization uncertainty as a function of position over the test range. These approaches are applied to model localization uncertainty for an AUV test range associated with the Victoria Experimental Network Under the Sea cabled observatory system, examining the factors mentioned above as well as the effects of water-column refraction and receiver geometry. [ABSTRACT FROM AUTHOR]

In this paper, we describe topology-efficient discovery (TED), the first designated topology-efficient discovery algorithm for underwater acoustic networks. Topology information is essential for network operations. By knowing the network topology, information sources can determine destination nodes and routing possibilities for their packets and schedule transmissions accordingly. It is therefore of interest to use a schedule for topology discovery to be used at the early stages of network operation. Such schedule must efficiently perform topology discovery and also guarantee a convergence time at the end of which topology discovery is complete and the network switches to its steady-state scheduling protocol. Considering this need, we offer a TED algorithm aimed to discover acoustic links and to assess their reliability. Designed to reduce the time overhead posed by the topology discovery phase, TED allows nodes to share time slots while controlling the number of possible collisions such that the delay of the topology discovery process is minimized. TED also discovers scheduling conflicts by discovering node pairs whose transmissions block one another, often referred to as near–far node pairs (NFNPs). Information about NFNPs can assist for power control or to increase channel utilization if interference cancelation techniques are employed. TED is applicable for underwater networks consisting of modems whose transmission range is on the order of a few kilometer and above, and when the maximal distance between the nodes is known to be large or assumed equal to the modem's transmission range. Numerical results show that under these conditions, TED accurately detects the network topology in a much shorter time compared to benchmark methods. The results also show that, using TED, more packets are received, and thus the accuracy in determining the reliability of communication links increases. We also report results from a sea experiment demonstrating the applicability of TED and its benefits in a real-world scenario. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) communications, Doppler estimation is one of the major stages in a receiver. Two Doppler estimation methods are often used: the cross-ambiguity function (CAF) method and the single-branch autocorrelation (SBA) method. The former results in accurate estimation but with a high complexity, whereas the latter is less complicated but also less accurate. In this paper, we propose and investigate a multibranch autocorrelation (MBA) Doppler estimation method. The proposed method can be used in communication systems with periodically transmitted pilot signals or repetitive data transmission. For comparison of the Doppler estimation methods, we investigate an orthogonal frequency-division multiplexing (OFDM) communication system in multiple dynamic scenarios using the Waymark simulator, allowing virtual UWA signal transmission between moving transmitter and receiver. For the comparison, we also use the OFDM signals recorded in a sea trial. The comparison shows that the receiver with the proposed MBA Doppler estimation method outperforms the receiver with the SBA method and its detection performance is close to that of the receiver with the CAF method, but with a significantly lower complexity. [ABSTRACT FROM AUTHOR]

This paper discusses the frequency–range interference patterns of the sound field in deep water based on ray theory when the source and the receiver are near the sea surface. Approximate formulas of the frequency–range interference patterns in the shadow zone are derived. In the shadow zone, the sound intensity periodic structure is caused by the interference among the bottom-reflected, bottom–surface-reflected, surface–bottom–surface-reflected, and bottom–surface-reflected rays. The sound intensity changes according to the frequency with two cycles, which increase with the propagation range but decrease with the source and the receiver depths. As a result, the range and depth of the broadband sound source can be extracted from the interference cycles of the sound intensity using a single hydrophone. Interference patterns due to sea-surface broadband noise sources in the shadow zone have been observed in two deep-sea experiments in the South China Sea. Given the high signal-to-noise ratio, the interference cycles can be extracted manually from the time–frequency surfaces. Then, the availability of single hydrophone localization in the shadow zone is verified by experimental data. Compared with the conventional passive source localization methods, this method does not require broadband guide sources, precise seafloor acoustical parameters, or intensive computation of replica fields. [ABSTRACT FROM AUTHOR]