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Ocean acoustic tomography (OAT) methods aim at estimating variations of sound speed profiles (SSP) based on acoustic measurements between multiple source-receiver pairs (e.g., eigenray travel times). This study investigates the estimation of range-dependent SSPs in the upper ocean over short ranges (<5 km) using the classical ray-based OAT formulation as well as iterative or adaptive OAT formulations (i.e., when the sources and receivers configuration can evolve across successive iterations of this inverse problem). A regional ocean circulation model for the DeSoto Canyon in the Gulf of Mexico is used to simulate three-dimensional sound speed variations spanning a month-long period, which exhibits significant submesoscale variability of variable intensity. OAT performance is investigated in this simulated environment in terms of (1) the selected source-receivers configuration and effective ray coverage, (2) the selected OAT estimator formulations, linearized forward model accuracy, and the parameterization of the expected SSP variability in terms of empirical orthogonal functions, and (3) the duration over which the OAT inversion is performed. Practical implications for the design of future OAT experiments for monitoring submesoscale variability in the upper ocean with moving autonomous platforms are discussed.

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Autonomous underwater vehicle (AUV) operations are limited by currently achievable underwater localization and navigation solutions; hence, the development of low-cost and passive (i.e., operable without an active power supply) acoustic underwater markers (or tags) can provide accurate localization information to AUVs improving their situational awareness, especially when operating in small scales or confined missions. This work presents an acoustic identification (AID) tag that can be powered wirelessly with ultrasonic power transfer from a remote acoustic source (e.g., mounted on an interrogating AUV) and provide localization information using backscatter communication. The AID tag harvests energy from the acoustic signal generated from the AUV and communicates by modulating the reflected signals from an embedded piezoelectric transducer. A scaled broadband AID tag prototype that achieves concurrent acoustic energy harvesting (tuned around 1.3 MHz) and backscatter communication (in wider frequency band 600 and 800 kHz) using frequency-domain multiplexing is implemented using a custom broadband impedance matching-based transducer design approach. During concurrent power and data operation, this prototype AID tag achieves data rates up to 200 kb/s using amplitude- and frequency-based modulation communication. The use of broadband schemes to achieve robust communications in low SNR (tested here down to -6 dB) is also demonstrated using linear frequency-modulated data carriers. Finally, the extension to full-scale devices of this AID tag concept and potential applications for short-range AUV routing and navigation such as homing and docking are discussed.

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This work investigates how vertical resolution affects the prediction of ocean sound speed through a suite of regional simulations covering the DeSoto Canyon in the Gulf of Mexico. Simulations have identical horizontal resolution of 0.5 km, partially resolving submesoscale dynamics, and vertical resolution from 30 to 200 terrain-following layers. The focus is on mesoscale eddies and how modeled sound speeds vary whenever more vertical baroclinic modes are resolved. While domain-averaged sound speed profiles do not differ substantively, the standard deviation increases for increasing resolution due to the sharper representation of mesoscale circulations underneath the mixed layer and their associated density anomalies.

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Underwater sound propagation is primarily driven by a nonlinear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g., eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSP variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This article investigates the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution [i.e., the neural adjoint (NA) method], which combines deep learning of the forward model with a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.

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Using a network of a few compact mobile underwater platforms, each equipped with a single acoustic sensor, as a distributed sensing array is attractive but requires precise positioning of each mobile sensor. However, traditional accurate underwater positioning tools rely on active acoustic sources (e.g., acoustic pingers), which implies additional hardware and operational complexity. Hence, self-localization (i.e., totally passive) methods using only acoustic sources of opportunity (such as surface vessels) for locating the mobile sensors of a distributed array appear as a simpler alternative. Existing underwater self-localization methods have mainly been developed for mobile platforms equipped with time-synchronized hydrophones and rely only on the time-differences of arrival between multiple pairwise combinations of the mobile hydrophones as inputs for a complex non-linear inversion procedure. Instead, this article introduces a self-localization method, which uses a linear least-square formulation, for two mobile time-synchronized vector sensor platforms based on their acoustic recordings of a distant surface vessel and their inertial navigation system (INS) measurements. This method can be generalized to multiple vector sensor pairs to provide additional robustness toward input parameter errors (e.g., due to a faulty INS) as demonstrated experimentally using drifting buoys with inertial vector sensors deployed ∼100 m apart in shallow water.

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Long-term noise interferometry analysis is conducted over six years of data using two hydrophones on the Ocean Observatories Initiative Cabled Array. The two hydrophones are separated by 3.2 km and are bottom-mounted at 1500 m. We demonstrate the ability of ambient noise interferometry to reliably detect multi-path arrivals in the deep ocean from bottom-mounted hydrophones. An analysis of the multi-path arrival peak emergence is presented, as well as long-term trends of the signal-to-noise ratio of the arrival peaks. Last, we show that long-term ambient noise interferometry provides the opportunity for monitoring directional, coherent ambient sound such as the fin whale chorus.

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The development of pre-deployed underwater infrastructures to aid in autonomous underwater vehicle (AUV) navigation is of keen interest, with the increased use of AUVs for undersea operations. Previous literature has introduced a class of passive underwater acoustic markers, termed acoustic identification (AID) tags [Satish, Trivett, and Sabra, J. Acoust. Soc. Am. 147(6), EL517-EL522 (2020)], which are inexpensive to construct, simple to deploy, and reflect unique engineered acoustic signatures that can be detected by an AUV instrumented with high-frequency sonar systems. An AID tag is built of multi-layer shells with different acoustic properties and thicknesses to generate a unique acoustic signature, composed of the multiple reflections created by the layer interfaces, thus akin to an "acoustic barcode." AID tags can be used as geospatial markers to highlight checkpoints in AUV trajectories or mark areas of interest underwater. This article investigates the optimization of the AID tag's design using energy based metrics and evaluates the detectability of an AID tag in the presence of interfering signals, such as clutter using matched-filter based techniques. Furthermore, experimental results of AID tags interrogated by a standard high-frequency sonar are presented to provide proof of concept of AID tag detection in a reverberant water tank.

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A library of broadband (100-1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated over nine days, is constructed using the ray-based blind deconvolution algorithm. Treating this CIR library either as data-derived replica for broadband matched-field processing (MFP) or training data for machine learning yields comparable ranging accuracy (∼50 m) for nearby vessels up to 3.2 km for both methods. Using model-based replica of the direct path only computed for an average sound-speed profile comparatively yields∼110 m ranging accuracy.

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A general blind deconvolution algorithmic framework is developed for sources of opportunity (e.g., ships at known locations) in an ocean waveguide. Here, both channel impulse responses (CIRs) and unknown source signals need to be simultaneously estimated from only the recorded signals on a receiver array using blind deconvolution, which is generally an ill-posed problem without any a priori information or additional assumptions about the underlying structure of the CIRs. By exploiting the typical ray-like arrival-time structure of the CIRs between a surface source and the elements of a vertical line array (VLA) in ocean waveguides, a principle component analysis technique is applied to build a bilinear parametric model linking the amplitudes and arrival-times of the CIRs across all channels for a variety of admissible ocean environments. The bilinear channel representation further reduces the dimension of the channel parametric model compared to linear models. A truncated power interaction deconvolution algorithm is then developed by applying the bilinear channel model to the traditional subspace deconvolution method. Numerical and experimental results demonstrate the robustness of this blind deconvolution methodology.

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A class of passive acoustic identification (AID) tags with curved symmetry for underwater navigation is presented. These AID tags are composed of radially stratified shells designed to backscatter a unique specular reflection pattern independent of the incidence orientation in a monostatic configuration, thus acting as acoustic bar-codes. The AID tag's response can be uniquely engineered by selecting the thicknesses and material properties of the individual constitutive shells. Furthermore, in the high-frequency regime, the specular component of the AID tag's response can be simply predicted numerically assuming horizontally stratified layers. This approach is demonstrated using scaled experiments with an AID tag constructed from 3D printed hemispherical shells.

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The ray-based blind deconvolution (RBD) technique for ocean waveguides estimates both the unknown waveform radiated by some source of opportunity and the channel impulse response (CIR) between the source and the receiving elements of an array of hydrophones using only measured signals, knowledge of the array geometry, and the local sound speed. Previous studies have investigated the applicability of this method for shipping sources in a shallow, nearly range-independent waveguide (∼200 m depth), but using a limited set of shipping vessels (typically only the research vessel itself) and operating within a small domain of RBD processing parameters (e.g., integration time and frequency band). This study systematically investigates the performance of the RBD method for estimating the CIR for a large set of shipping vessels recorded on short aperture, bottom-mounted, vertical arrays deployed in the Santa Barbara channel across different frequency bands and integration times, and also in comparison to CIR measured using active sources. Furthermore, the influence of the source motion on the RBD algorithm is quantified both numerically and experimentally.

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The original ray-based blind deconvolution (RBD) algorithm estimates the phase of a source of opportunity-e.g., a shipping vessel-using conventional wideband beamforming (CWBF) along a well-resolved ray-path to estimate the channel impulse response (CIR). However, for the case of an anisotropic radiator and when using a short-aperture receiving array, CWBF is unable to accurately beamform on weak multipath arrivals. Instead, a higher-resolution RBD algorithm based on single snapshot adaptive beamforming is introduced by using space-frequency smoothing of the array covariance matrix; and its performance is demonstrated using experimental recordings of a container vessel to estimate the bottom-bounce arrival of the CIR.

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The ray-based blind deconvolution algorithm can provide an estimate of the channel impulse responses (CIRs) between a shipping source of opportunity and the elements of a receiving array by estimating the unknown phase of this random source through wideband beamforming along a well-resolved ray path. However, due to the shallow effective depth (typically <10 m) and low frequency content (typically less than a few kHz) associated with shipping sources, the interfering direct and surface arriving pair and subsequent bottom and surface-bottom arrival pair cannot always be resolved in the CIR arrival-time structure. Nevertheless, this study demonstrates that the bottom reflection loss can be inferred from the ratio of the magnitude spectra of these two arrival pairs if a frequency-dependent correction (which can be purely data based) is applied to correct for the dipole source effect. The feasibility of the proposed approach is demonstrated to invert for the geoacoustic parameters of a soft-layer covering the ocean floor using a nonlinear least-square algorithm.

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Acoustic power transfer (APT) for wireless electronic components has received growing attention as a viable approach to deliver power to remotely located small electronic devices. The design of an efficient APT system requires accurate models to describe its individual components as well as the interaction between them. Most of the analytical models available to represent the bulk piezoelectric transducers used in APT are limited to either thin rod or thin plate transducers. However, transducers with moderate aspect ratios are often used, especially at the receiver end. In this work, in addition to reviewing standard theories, models based on the Rayleigh and Bishop rod theories are developed to analyze transducers [transmitter (TX) or receiver (RX)] with various aspect ratios. Results from these models are compared with experimental data and finite-element analysis to determine the range of aspect ratios in which they are valid. In addition, fluid loading effects on the predictions of all models are investigated, and the generated pressure fields by the transducers with different aspect ratios are compared. The resulting models are used to analyze the effect of aspect ratio on the performance of the transducer when operated as a TX or an RX in an APT setting.

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Ambient noise cross-correlations between separated sensors can yield estimates of the Green's function between them. Vector sensors (which record both pressure and acoustic velocity vector components) can leverage their directionality to reject ambient noise sources that do not contribute to the emergence of the Green's function, thus improving performance over standard omnidirectional hydrophones. To quantify this performance gain, a time-domain analytical expression for the correlation between each component of a vector sensor in the presence of an isotropic ambient noise field is derived. Improvement of the velocity channel correlations relative to pressure channel correlations is examined for varying bandwidth, sensor separation distance, and additive channel noise levels. Last, the experimentally measured reduction in variance for the velocity channels correlations vs pressure correlations, using drifting vector sensors deployed in the Long Island Sound, were found to be comparable to the theoretical prediction. Overall, both theoretical and experimental results indicate modest gains are obtained when extracting the Green's function from velocity correlations over using pressure correlations. Thus, vector sensors can be used to reduce the required averaging time for this noise correlation processing, which may be especially useful, for instance, in a fluctuating environment or for drifting sensors.

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Autonomous Underwater Vehicle (AUV) navigation requires accurate positioning information from the environment. Existing underwater navigation paradigms employ active acoustic transponders that assist in this task, but these more complex and costly systems require maintenance and power. This paper presents instead a passive underwater marker made of different horizontally stacked acoustically reflective materials that is cost effective and relatively simple to service. A marker's characteristic acoustic signature can be detected by AUVs as acoustic backscattering upon tag insonification, and hence be used for navigation purposes.

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Currently, noninvasive cost-effective techniques capable of quantifying non-uniform degradation of tendon's mechanical and structural properties associated with localized tendon injuries are not readily available. This study demonstrates the applicability of a simple surface-wave elastography (SURF-E) method for assessing the stiffness of the Achilles Tendon by measuring the propagation velocity of surface waves along the tendon in a much broader range of values than currently available Ultrasound-based or MRI-based elastography methods do. Results from this study confirm the non-uniform stiffening of the AT during passive ankle dorsiflexions.

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A hybrid boundary element model is proposed for the simulation of large piezoelectric micromachined ultrasonic transducer (PMUT) arrays in immersion. Multiphysics finite element method (FEM) simulation of a single-membrane structure is used to determine stiffness and piezoelectrically induced actuation loading of the membranes. To simulate the arrays of membranes in immersion, a boundary element method is employed, wherein membrane structures are modeled by a surface mesh that is coupled mechanically by mass, stiffness, and damping matrices, and acoustically by a mutual impedance matrix. A multilevel fast multipole algorithm speeds up computation time and reduces memory usage, enabling the simulation of thousands of membranes in a reasonable time. The model is validated with FEM for a small 3 3 matrix array for both square and circular membrane geometries. Two practical optimization examples of large PMUT arrays are demonstrated: membrane spacing of a 7 7 matrix array with circular membranes, and material choice and top electrode coverage of a 32-element linear array with 640 circular membranes. In addition, a simple analytical approach to electrode optimization based on normal mode theory is verified.

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This article presents a method for improving the performance of the ray-based blind deconvolution (RBD) algorithm, which was first proposed by Sabra, Song, and Dowling [J. Acoust. Soc. Am. 127(2), EL42-EL47 (2010)]. In order to retrieve the channel impulse response (CIR), the original RBD algorithm uses the source signal phase from a selected single beam output. However, when the impinging multipath signals have low coherence, the channel estimate from a selected beam may not show all paths correctly. In this research, the maximum likelihood estimator, which is called the alternating projection, is applied to separate multipath signals. Then the multiple CIRs obtained from those separated signals are coherently combined. This results in more robust detection of existing multipaths. The performance of the proposed method is verified using Noise09 sea experiment data, where the proposed method better resolves the multipath arrival structure.

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This paper presents a technique for solving the multichannel blind deconvolution problem. The authors observe the convolution of a single (unknown) source with K different (unknown) channel responses; from these channel outputs, the authors want to estimate both the source and the channel responses. The authors show how this classical signal processing problem can be viewed as solving a system of bilinear equations, and in turn can be recast as recovering a rank-1 matrix from a set of linear observations. Results of prior studies in the area of low-rank matrix recovery have identified effective convex relaxations for problems of this type and efficient, scalable heuristic solvers that enable these techniques to work with thousands of unknown variables. The authors show how a priori information about the channels can be used to build a linear model for the channels, which in turn makes solving these systems of equations well-posed. This study demonstrates the robustness of this methodology to measurement noises and parametrization errors of the channel impulse responses with several stylized and shallow water acoustic channel simulations. The performance of this methodology is also verified experimentally using shipping noise recorded on short bottom-mounted vertical line arrays.