Karim G. Sabra

Acoustic transducers are essential for object localization and environmental sensing. Conventional transducers rely on piezoelectric crystals, whose acoustic-electric response is fixed by the crystal lattice's inherent asymmetry and orientation. This results in static coupling behavior, necessitating bulky arrays of rigid elements with complex wiring and high computational demands for directional sensing. Here, we report a fundamentally new class of acoustic-electric coupling that emerges from topology-governed charge transport in 3D micro-architected piezoelectric metamaterials. Unlike single crystals, these architected materials exhibit dynamic, geometry-driven electromechanical responses. Acoustic waves excite multiple coupled vibration modes, enabling selective amplification, suppression, or reversal of charge flow based on the incident wave's frequency, direction, and the material's topology. This tunable, symmetry-breaking response is encoded not in the chemistry but in the architecture-representing a shift from crystal-defined to structure-programmed piezoelectricity. We further demonstrate that a single metamaterial transducer can perform frequency-dependent beam shaping without changing aperture size or requiring mechanical adjustment. Combined with machine learning and 3D printing, these intelligent acousto-electrical metamaterials (IAM) enable real-time localization of multiple moving sound sources. This approach lays the foundation for compact, adaptive, and intelligent acoustic sensing systems across a range of applications-from autonomous vehicles to medical imaging and underwater robotics.

This paper investigates the use of passive high-frequency (HF) noise interferometry for sea surface roughness estimation and Bragg-line detection using ambient noise sources of opportunity. We model the sea-surface backscatter channel as a slowly time-varying tapped-delay line driven by incident HF noise and show that the resulting correlation structure supports delay– Doppler processing analogous to active noise radar. A link-budget analysis decomposes the achievable signal-to-noise ratio (SNR) into contributions from (i) the fractional coherent noise power, (ii) the effective radar cross section (RCS) of the sea surface, and (iii) the two-way propagation loss. We apply this framework to experimental data from Kwajalein Atoll, where an HF broadcast transmitter provides a strong coherent noise source. Using conventional beamforming followed by passive correlation processing, we observe Bragg surface scattering in agreement with our link-budget predictions. We further demonstrate that projection-based processing, which removes zero-Doppler clutter from the reference path, enables clearer detection of Bragg lines in a bistatic configuration between the KWOTHR and Meck receive arrays using a distant non-cooperative source. The results indicate that HF ambient noise sources can support sea-surface roughness estimation in a fully passive sensing architecture.

The effects of topographically generated lee waves on acoustic propagation near the Atlantis II seamount in the New England Seamount area are investigated using the Coastal and Regional Ocean Community model. Submesoscale permitting ocean circulation simulations were conducted to model the formation and characteristics of lee waves, analyze their hydrographic impact on sound speed profiles, and assess their influence on sonic layer acoustic duct (SLAD) propagation. Lee waves significantly alter sound speed profiles, affecting the deep sound channel and the sonic layer depth. These alterations lead to notable variations in acoustic propagation, blocking energy from the SLAD at locations where lee waves lift the sonic layer depth and have effects in the low to mid-frequency range (1000-2000 Hz). The influence of the temporal variability of the lee waves on the resulting sound speed profile and SLAD fluctuations is illustrated. Finally, 3D ray-tracing simulations highlight substantial spatial variability in transmission loss depending on the relative orientation of the acoustic source and the wavefronts of the lee waves. Overall, these results indicate the utility of high-resolution ocean models for investigating the generation of lee waves and the resulting spatial and temporal modulations of the ocean sound speed field affecting underwater acoustic propagation.

A generative deep learning framework is introduced for ray-based ocean acoustic tomography (OAT), an inverse problem for estimating sound speed profiles (SSP) based on arrival-times measurements between multiple acoustic transducers, which is typically ill-posed. This framework relies on a robust low-dimensional parametrization of the expected SSP variations using a variational autoencoder and a linear dynamical model as further regularization. This framework was tested using SSP variations simulated by a regional ocean model with submesoscale permitting horizontal resolution and various transducer configurations spanning the upper ocean over short propagation ranges and was found to outperform conventional linear least squares formulations of OAT.

This work demonstrates the feasibility of using autonomous surface vehicles equipped with a shallow towed acoustic module (TAM) to survey the spatial variability of low-frequency acoustic propagation across complex bathymetry, such as the Atlantis II seamounts in the Northwest Atlantic. The abrupt seamount topography is found to significantly influence the TAM's recordings of chirp transmissions (500-600 Hz band) from a bottom-moored source ∼30 km from the seamounts by notably causing blockage of in-plane propagation paths and complex reverberation arrivals displaying three-dimensional effects, as confirmed by synthetic aperture beamforming. Ray tracing simulations are compared to these observations based on a data-assimilated ocean model.

Long-range detection and localization in deep water underwater sources with moving platforms typically rely on estimating the angle of arrivals at low frequency (i.e., associated with large wavelengths). Traditional methods typically use a long towed array of hydrophones to provide the required angular directivity at low frequency. However, such arrays are typically costly and complex to operate and require a powerful vehicle capable of towing such arrays. Here, an alternate method is demonstrated using a small autonomous surface vehicle platform capable of low-frequency source localization. The vehicles—Liquid Robotics Wave Gliders—are equipped with a compact four-element tetrahedron-shaped hydrophone array and a CTD suite capable of profiling to 150 m depth. These vehicles were deployed above the New England Seamounts in 2023 and 2024 and recorded low-frequency (200–300 Hz) transmission at ranges upwards of 200 km from bottom-moored sources deployed in the SOFAR channel. Pressure gradient-based signal processing methodology is applied to the compact tetrahedron hydrophone array recordings to emulate the low-frequency angular directionality performance of a vector sensor. The influence of environmental variability and seamount bathymetry on the estimated elevation and azimuthal angles with this compact array is investigated experimentally and numerically using ray-tracing simulation. [Work sponsored by ONR.]

Using a network of autonomous underwater platforms as a distributed coherent sensing array requires precise positioning of each sensor node. Here, a self-localization (i.e., totally passive) method using only acoustic sources of opportunity (such as surface vessels) to locate the mobile sensor nodes is presented as an alternative to conventional active short or long baseline systems. Existing underwater self-localization methods have mainly been developed for mobile platforms equipped with time-synchronized hydrophones and rely only on the time difference of arrivals between multiple pairwise combinations of the mobile hydrophones as inputs for a complex non-linear inversion procedure. Instead, we present a three-dimensional self-localization method using a linear least square formulation between mobile time-synchronized underwater platforms equipped with a compact directional hydrophone array based on their acoustic recordings of a distant surface vessel and their inertial navigation systems measurements. The influence of acoustic refraction and environmental variability on the performance of this self-localization method is investigated using deep water acoustic data collected near the Atlantis II seamounts in the Northwest Atlantic by two Wave Gliders instrumented with underwater towed acoustic modules located above and below the sonic layer depth for separation distance up to a few kilometers. [Work sponsored by ONR.]

Detection of underwater targets in the upper ocean is often influenced by surface acoustic ducts, which enable long-range guided sound propagation. The physics of surface acoustic duct propagation, notably its minimum cutoff frequency, has been extensively studied in past studies. However, quantifying the influence of environmental parameters on the temporal and spatial variability of the sonic layer depth (SLD) and the associated energy leakage below the SLD remains under investigation. A collaborative experiment was conducted in August 2024 during the NESMA-IOP 2 cruise to address this topic. A dual-frequency band (∼130 Hz and ∼1.3 kHz) acoustic source was deployed ∼15 m deep from the drifting R/V Revelle while continuously profiling the water column down to 350 m with a fast CTD system to capture the local sub-mesoscale and internal-wave ocean variability. Simultaneously, two autonomous surface vehicles, called Wave Gliders (WG), each instrumented with a towed acoustic module, located above and below the effective sonic layer depth (∼50 m) at ∼12 and ∼100 m, recorded the source transmissions at ranges between 1 and 10 km. A third WG profiled the water column down to 150 m with a CTD assessing the spatial variability of the SLD. Experimental detection ranges are compared to numerical predictions. [Work sponsored by ONR.]

Ambient noise monitoring in the ocean can provide insight into physical oceanographic processes such as wind and wave generation. Additionally, accurate environmental parametrization of the noise levels is needed for SONARsystems performance prediction. Typically, passive acoustic monitoring is conducted with stationary buoys that can record for long periods of time or large hydrophone arrays towed by manned vessels, creating a trade-off between monitoring area and mission duration. Here, an autonomous surface vehicle platform (Liquid Robotics Wave Gliders—WG) instrumented for physical oceanography measurements and also outfitted with a compact four-element tetrahedron-shaped hydrophone array is presented. Multiple WGs were deployed in the vicinity of the Atlantis II seamount to perform ambient acoustic surveys at depths ranging from 10 to 150 m. During these geo-localized surveys, the measured relationship between mid-frequency ambient noise levels and local wind speed was found to be consistent with previous experiments, demonstrating the efficacy of the Wave Glider as an ambient noise monitoring platform. Additionally, the mid-frequency beamforming capabilities of their compact array are leveraged to estimate in-situ the effective sound pressure level of the local sea-surface noise sources and its parametrization on the local wind speed which is needed for ambient soundscape modeling. [Work sponsored by ONR.]

Accurate positioning is critical for autonomous underwater vehicles (AUVs), particularly during homing and docking. Passive Acoustic Identification (AID) tags offer a scalable, cost-effective alternative to active acoustic and optical methods, excelling in turbid and cluttered environments. These tags guide AUVs by generating distinct acoustic signatures to improve localization and navigation accuracy. This study demonstrates long-range experiments with larger AID tags (up to 24-inch diameter) in a lake environment, tested at frequencies between 100 and 600 kHz. Results show detectability at distances exceeding tens of meters, with optimized signal processing techniques enhancing robustness in cluttered conditions. Furthermore, an active tag design for wireless backscatter communication was implemented. Powered by ultrasonic energy transfer from an interrogating AUV, the tag harvests energy and communicates data via single-frequency modulation, achieving data rates of up to 200 kb/s even in low signal-to-noise environments. These advancements establish AID tags as a dual-purposesolution for improving AUV localization and enabling robust communication in complex underwater missions.

Deepwater abrupt topography changes, such as seamounts, can significantly enhance the complexity of underwater sound propagation by notably creating highly variable three-dimensional scattering effects, thus making it more challenging to predict numerically. One avenue to precisely survey the spatial variability of deep-water sound propagation induced by isolated seamounts is to use instrumented autonomous surface vehicles (ASV) that can be accurately geo-located. These precise acoustic observations can, in turn, be used to validate numerical model predictions in these complex environments. Here, an ASV called Wave Glider was equipped with a hydrodynamic towed acoustic module (TAM) to survey the spatial variability of low-frequency acoustic propagation across the Atlantis II seamounts in the Northwest Atlantic. The TAM was deployed along the Gulf Stream boundary and crossed over the Atlantis II seamounts, which significantly influenced the TAM’s recordings of chirp transmissions (500–600 Hz band) from a bottom-moored source ∼30 km from the seamounts by notably causing blockage of in-plane propagation paths and complex reverberation arrivals displaying three-dimensional effects, as confirmed by synthetic aperture beamforming. 2-D and 3-D ray-tracing simulations are performed with input sound speed fields computed from the outputs of a data-assimilated ocean model to compare with experimental observations. [Work Supported by ONR.]

Bill Kuperman’s contributions to the field of underwater acoustics have been numerous and tremendous. I would like to share some memories of my time working with Bill starting in 2003 as a Postdoc under his mentoring at the Marine Physical Laboratory (Scripps Institute of Oceanography, UC San Diego). At that time, Bill was investigating the use of coherent processing of ocean and seismic ambient noise as a novel means to perform passive remote sensing, i.e., without using conventional active sources. Bill’s intuition, charisma, leadership, scholarly experience, and constant encouragement to explore new research avenues created a unique work environment at the Marine Physical Laboratory, which I greatly benefited from, and I am most grateful for.

This study evaluates the impact of ocean model vertical resolution in representing three-dimensional (3D) sound speed variability used for acoustic predictions in the upper ocean. Sound propagation is investigated for two configurations of the same regional ocean circulation model of the De Soto Canyon in the northern Gulf of Mexico. Both configurations employ a submesoscale-permitting horizontal resolution of 0.5 km but differ in vertical resolution, featuring 30 or 200 terrain-following layers. The higher vertical resolution is found to better represent oceanographic features, such as submesoscale fronts and salt-fingering staircases, which are crucial for accurately predicting 3D sound speed variability in the upper ocean, especially below the mixed layer. Additionally, ray-tracing and coupled normal mode simulations indicate that such oceanographic features, predicted by the higher vertical resolution configuration, can significantly affect acoustic propagation in the upper ocean for the tested frequencies in the band [500-1500 Hz], even at relatively short ranges (<30 km). These results indicate the potential of high-resolution regional ocean models for improving the accuracy of acoustic forecasts in the presence of submesoscale ocean variability, notably for operational oceanography and naval applications.

Developing persistent and smart underwater markers is critical for improving navigation accuracy and communication capabilities of autonomous underwater vehicles (AUVs). A wireless acoustic identification tag, which uses a piezoelectric transducer tuned in the broadband ultrasonic range (200-500 kHz), was experimentally demonstrated to achieve highly efficient power transfer (source-to-tag electrical power efficiency of >2% at 6 m) and concurrent high data rate and backscatter level communication (>83.3 kbit s-1, >170 dB sound pressure level at 6 m) with potential operating range ≈ 10 m based on analytical extrapolations. Parameter selection considerations dictated by the desired range and data-rate requirements in communication are presented. The transducer piezoelectric element selection, impedance matching approach, and simulation-based circuit optimization for frequency multiplexed operation are also detailed. Experimental tests benchmarking performance sensitivity to source and tag misalignment are introduced and implications for AUV operations are discussed.

This work demonstrates the feasibility of performing through-the-sensor (TTS) sub-bottom imaging using low-frequency ([100 Hz-1kHz]) self-noise generated by the propulsion of an autonomous underwater vehicle (AUV) acting as a source of opportunity. The self-noise was recorded by a short towed horizontal line array (11.4 m aperture) by the same AUV while it operated ∼35 m above the seabed along a range-dependent section at the New England shelf break. The seabed and sub-bottom layers imaged by this TTS method were found to be consistent with the images simultaneously obtained at higher frequency [2.5-4.5 kHz] using a conventional active source mounted on the AUV.

Navigation of autonomous underwater vehicles requires accurate positioning information, notably during docking and homing operations. This letter demonstrates the feasibility of using a constellation of passive Acoustic Identification (AID) to enable accurate localization of a docking station by an of autonomous underwater vehicle. Scaled experiments are conducted using a pair of AID tags composed of multiple concentric hemispherical acrylic layers, each of which generates a unique backscattered acoustic signature when ensonified by a broadband ultrasonic transducer. A parameterized signal processing detection methodology is implemented to improve the detectability of AID tags in the presence of clutter.

Abstract This study investigates the complex interactions between the North Atlantic Current (NAC) and the New England Seamount chain, focusing on the Atlantis II seamount. Employing a high‐resolution submesoscale permitting regional ocean circulation model nested within a basin‐wide simulation, it explores three distinct periods, each 2 weeks long, showcasing varied deep mesoscale and submesoscale circulations around the seamount. The analysis includes Eulerian statistics and Lagrangian particle tracing experiments to explore the transport and mixing impacts of the highly dynamic flow around the seamount. The results reveal significant density variations across the water column, attributed to the seamount's influence on local ocean currents. Specifically, mesoscale and submesoscale vortices, a seamount wake, and lee waves form during periods of increased near‐bottom current flow. These findings highlight the critical role of topographic features in modulating oceanic flows and ocean mixing, which have implications, among others, for nutrient distribution, acoustic propagation, and climate modeling. The variety and variability of the mesoscale and submesoscale circulations that arise in response to the variability in the NAC strength and position in relation to the New England Seamount Chain demonstrate the difficulty in extrapolating general behaviors from isolated observational campaigns.

This Letter investigates the influence of source motion on the performance of the ray-based blind deconvolution algorithm (RBD). RBD is used to estimate channel impulse responses and source signals from opportunistic sources such as shipping vessels but was derived under a stationary source assumption. A theoretical correction for Doppler from a simplified moving source model is used to quantify the biases in estimated arrival angles and travel times from RBD. This correction is numerically validated using environmental data from the SBCeX16 experiment in the Santa Barbara Channel. Implications for source localization and potential passive acoustic tomography using RBD are discussed.

This study investigates the impact of three-dimensional (3-D) bathymetry and sound speed variability on sound propagation, utilizing 3-D ray tracing compared to simple two-dimensional ray tracing (Nx2-D) within the New England seamount chain, focusing on the Atlantis II seamount area. The research has three main objectives. First, it examines the differences between 3-D and 2-D acoustic propagation modeling of bathymetric reflections. Second, it evaluates the impact of physical oceanographic effects on 3-D versus 2-D acoustic modeling, employing a high-resolution regional ocean circulation model with a horizontal resolution of 1 km and 100 terrain-following vertical layers. Third, it compares the modeled acoustic propagation to experimental data collected by a surface autonomous vehicle (wave glider) fully instruments for measuring local physical ocean variables (e.g., CTD data) and equipped with a compact tetrahedron hydrophone array (capable of profiling the water column from ∼10 to ∼150m) which recorded low and mid-frequency transmissions from moored acoustic sources deployed near Atlantis II seamount. The need for full 3-D ray-tracing modeling (instead of using simpler Nx2-D simulations) for accurately predicting shadow zones and understanding regional acoustic propagation paths in areas with rapid and diverse bathymetric changes, such as isolated seamounts, will be quantified.

The rapid evolution of the physical properties of the upper ocean expected in regions with strong submesoscale features can lead to complex acoustic propagation effects. In spring 2023, an autonomous surface vehicle (LRI Wave Glider) was outfitted with a suite of oceanography and acoustic equipment and deployed in the Gulf Stream in a region located over the New England Seamounts, allowing an opportunity to study how the physical variability can affect the speed of sound and ambient soundscape. In August 2024, three more Wave Gliders were deployed in the same area. The vehicles, using GPS and time-synchronized equipment, were able to collect ambient acoustic data on a four-element hydrophone array, as well as local physical properties (e.g. CTD data, wind speed, and ADCP current data) that could affect the acoustic recordings during long term missions above and around the Atlantis II seamount. Two low frequency sources of opportunity were also able to be recorded over long distances, with noise level varying with vehicle position, allowing an understanding of acoustic propagation paths in this area. These long-term measurements demonstrate the unique capability of an autonomous surface vehicle for making wide-range acoustic surveys.

Accurately modeling acoustic propagation in dynamic ocean environments exhibiting sub-mesoscale variability is challenging. Such environments require high-resolution ocean circulation model to accurately predict local sound speed variability at sub-mesoscales. This study focuses on high-resolution rendition of the De Soto canyon area, located in the Gulf of Mexico. Predicted sound speed profiles were found at times to exhibit very strong vertical gradients (e.g. with sharp “elbows”) reflecting the small-scale vertical variations of ocean layer properties. If used as raw inputs for ray-tracingsimulations, such sound speed profiles were found to generate inconsistent estimation of eigenray paths and spurious paths appearing intermittently caused by the inherent limitations of the ray-tracing methods. Nevertheless, we show that such artefacts can be mitigated by appropriately smoothing the input sound speed profile spatial variability especially if only low to mid-frequency (&amp;lt;1 kHz) propagation is predicted. To do so, the accuracy of arrival-times predictions using ray-tracing predictions with spatially smoothed sound speed profiles as a function of center frequency (&amp;lt;1 kHz) will be quantified against parabolic equations simulations used here as reference solution. We hypothesize that this smoothing approach for the input sound speed variations will enable to retain the numerical advantage of ray-tracing predictions while maintaining sufficient accuracy.

Accurate positioning is essential for the navigation of autonomous underwater vehicles (AUVs), especially during docking and homing. This presentation explores the feasibility of using passive Acoustic Identification (AID) tags for precise AUV localization at a docking station. As a proof of concept, scaled experiments used AID tags made of four concentric hemispherical acrylic layers, each creating a unique backscattered acoustic signature when ensonified by a broadband ultrasonic transducer in GaTech water tank. The tags in these tests have a 9-in. outer diameter and were tested at a frequency range of 700 kHz to 1.3 MHz over a distance of up to 6 m. To enhance detection in cluttered environments, a parameterized signal processing method was employed. Additionally, we will present analysis of long-range experiments to assess the tags' detectability at greater distances using larger AID tags with outer diameter of up to 24-in. outer diameter. These tests will operate at a frequency range of 100 to 600 kHz, aiming to reach distances of tens of meters. Implications for the performance of AID tags operating under real-life conditions will be discussed.

Abstract Measuring the temperature changes of the deep ocean will be critical to understanding how the earth system will respond to climate change. In this work, we present a method for measuring the depth‐averaged, deep ocean temperature at local (∼3 km) spatial scales using passive estimates of acoustic propagation. These passive acoustic estimates of deep ocean temperature can be used with existing and future passive acoustic monitoring infrastructure to provide complimentary observations of the ocean to in situ measurements, and could be particularly useful in areas of poor ocean observation coverage. Using 8 years of ambient sound data, we demonstrate that the passive estimates agree with global ocean models and measurements by ARGO floats. The rms difference between the HYCOM ocean model is shown to be 0.13°C, and the rms difference between ARGO measurements is shown to be 0.086°C.

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.

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.

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.

Inverse problems describe the task of recovering an underlying signal of interest given observables. Typically, the observables are related via some non-linear forward model applied to the underlying unknown signal. Inverting the non-linear forward model can be computationally expensive, as it often involves computing and inverting a linearization at a series of estimates. Rather than inverting the physics-based model, we instead train a surrogate forward model (emulator) and leverage modern auto-grad libraries to solve for the input within a classical optimization framework. Current methods to train emulators are done in a black box supervised machine learning fashion and fail to take advantage of any existing knowledge of the forward model. In this article, we propose a simple learned weighted average model that embeds linearizations of the forward model around various reference points into the model itself, explicitly incorporating known physics. Grounding the learned model with physics based linearizations improves the forward modeling accuracy and provides richer physics based gradient information during the inversion process leading to more accurate signal recovery. We demonstrate the efficacy on an ocean acoustic tomography (OAT) example that aims to recover ocean sound speed profile (SSP) variations from acoustic observations (e.g. eigenray arrival times) within simulation of ocean dynamics in the Gulf of Mexico.

Autonomous underwater vehicle (AUV) navigation requires accurate positioning information from the surrounding environment, especially for tasks such as AUV homing or docking operations. Previous literature has introduced a class of low-cost passive underwater acoustic markers, termed Acoustic IDentification (AID) tags [Satish and Sabra, J. Acoust. Soc. Am. 149(5), 3387–3405 (2021)] built of multi-layer shells with different acoustic properties and thicknesses to generate a uniquely engineered acoustic signature, composed of the multiple reflections created by the layer interfaces. These passive AID tags can be detected by an AUV instrumented with a high-frequency sonar transducer at significantly greater distances than conventional optical methods, especially in turbid waters. Additionally using AID tags as navigation-aid can also alleviate the need of relying on active acoustic transponders. An implementation of a constellation of AID tags enabling fine underwater positioning an AUV towards a docking station or for homing purposes will be presented to provide proof of concept. Furthermore, the optimization of the design of the AID tags for this application aswell as specific signal processing detection methodologies to improve thedetectability of AID tags in the presence of interfering signals (e.g., clutter) will be discussed. [Work supported by the Office of Naval Research].

The ocean covers 70% of the Earth surface area, yet over 80% of it remains unexplored despite the advances in underwater acoustics and oceanography. Ocean exploration is critical for accurate climate model development, renewable energy applications, and in understanding the marine habitat. Further exploration necessitates improvements in underwater navigation with Autonomous Underwater Vehicles (AUVs). Utilizing acoustic landmarks can enhance AUV localization performance [Fula et al., Oceans IEEE/MTS (2018)]. Passive markers called Acoustic Identification (AID) Tags have unique and identifiable acoustic reflection signatures designed to function as landmarks [Satish et al., JASA 149 (2021)]. These targets can be detected by Match Filtering returns with template signals. Match Filtering performance is limited in the presence of strong interfering signals, and with changes in sound speeds that alters the temporal structure of these signatures. The application of a Machine Learning classifiers for detecting AID tag signatures can improve the localization performance. Through implementation of Logistic Regression, Deep Neural Networks, and Convolutional Neural Networks, the generalizability and superiority of these models is demonstrated. We report high accuracies (&amp;gt;90 %) in a multilabel (8 classes) classification task with signals with low SNR (–6 dB) and strong interference (+12 dB).

Accurate knowledge of the spatial and temporal evolution of the three-dimensional ocean sound speed profile (SSP) is crucial for underwater source localization. Ocean acoustic tomography (OAT) methods aim at reconstructing SSPs variations based on acoustic measurements between multiple source-receiver pairs (e.g., eigenrays arrival times). This study investigates the estimation of range-dependent SSPs using a classical model-based OAT method (i.e., ray-based ocean acoustic tomography), for various configurations of source and receiver configurations using autonomous platforms in a highly-dynamic upper ocean environment. A regional oceanographic simulation of the De Soto Canyon circulation in the Gulf of Mexico is used to construct 3D sound speed variations spanning a month long which exhibits significant sub-mesoscale variability. Two main aspects affecting OAT performance in the presence of high 3D SSPs variability are investigated: (1) The influence of the input acoustic data (i.e., source-receivers configuration and platform motion, arrival-times accuracy...) and (2) the actual implementation of the OAT scheme (i.e., selection of complexity reduction basis, linearized forward model assumptions as well as the use of iterative solvers) on SSPs estimations errors. Practical implications for the design of OAT experiments in the dynamic upper ocean will be discussed. [Work supported by the Office of Naval Research.]

To enhance functionality of Acoustic Identification Tags (AID), passive markers designed as landmarks for Autonomous Underwater Vehicles (AUVs) [Satish et al., JASA 149 (2021)], the development of an accompanying battery free active communication platform was explored [Bhardwaj et al., JASA 152, 4 (2022)]. This platform utilizes custom piezoelectric transducers impedance matched for broadband operation, enabling concurrent acoustic energy harvesting and fast backscatter communication [Allam et al., SPIE 31, 9 (2022)]. The combination of these active and passive devices can improve AUV localization and information transfer for specific applications such as short-range search missions or AUV homing and docking. The design of this platform for underwater operations requires a balance of parameters such as center frequency and piezoelectric element dimensions to achieve a high range of operation, power efficiency and data rates, while minimizing attenuation and spreading losses challenges. The design process for piezo selection, acoustic and electrical impedance matching, transducer casing design, and the experimental evaluation of the chosen 350 kHz design will be discussed. This approach was validated experimentally to quantify performance metrics such as the maximal range, data rates using various standard communication such as Frequency Shift Keying (FSK) and Amplitude Modulation (AM).