Problems In Underwater Acoustics Research Articles

Understanding and applications of striation-based beamforming in underwater acoustics

Utilizing the shallow water acoustic interference phenomenon in the range-frequency domain, striation-based beamforming (SBF) was proposed to estimate waveguide invariant and enhance active sonar output. In the past decade, SBF has also been applied to enhance LOFARgrams to improve tracking performance. In this paper, the correlation theory is used to understand SBF, and some further applications of SBF in underwater acoustic problems are also discussed. It shows that SBF essentially selects the optimal signal components with the best correlation. During the process, the correlation of array signals is improved and the ability to converge signals arriving via different paths is achieved to ensure array gain. The level of gain improvement is related to the direction of striation-based processing. Hence, a cost function can be constructed to facilitate the implementations of SBF in tasks such as striation slope estimation and source range estimation. Additionally, the SBF technique can be further applied to estimate the Green's function by introducing the cross-correlation preprocessing. Utilizing the mathematical connection between SBF and CBF, SBF can also be naturally extended to other beamforming algorithms and improve their performance, such as the subspace algorithms. The above is explained using simulation and experimental results.

The edge-based smoothed FEM with [formula omitted]-Bathe implicit temporal discretization scheme for the analyses of underwater wave propagation problems

The underwater acoustic wave propagation problem is an important issue in ocean engineering field. The classical finite element approach with edge-based smoothed gradient smoothing technique is incorporated into the novel ρ∞-Bathe implicit direct time integration scheme in this paper for solving underwater transient acoustic wave propagation problems. This edge-based smoothed FEM (ES-FEM) and ρ∞-Bathe method are employed to perform the space and time domain discretization, respectively. Due to the appropriate softening effects from the ES-FEM, the space discretization errors can be markedly reduced. Meanwhile, the possible numerical error related to the temporal discretization also can be significantly suppressed by the novel ρ∞-Bathe method which possesses the controllable numerical damping effects. Through detailed analysis of the total dispersion error, we find that the numerical results from the proposed scheme can converge monotonically to the exact solutions when employing the decreasing temporal discretization intervals and the possible numerical anisotropy effects are also effectively suppressed. These excellent numerical performance clearly distinguish the present method from the conventional approaches and make it to be particularly suitable for complicated acoustic wave propagation analysis. Finally, various typical numerical experiments are performed to illustrate the capacities of the proposed scheme in tackling underwater transient acoustic wave propagation problems.

Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder

Passive localization of underwater targets was a thorny problem in underwater acoustics. For traditional model-driven passive localization methods, the main challenges are the inevitable environmental mismatch and the presence of interference and noise everywhere. In recent years, data-driven machine learning approaches have opened up new possibilities for passive localization of underwater acoustics. However, the acquisition and processing of underwater acoustics data are more restricted than other scenarios, and the lack of data is one of the most enormous difficulties in the application of machine learning to underwater acoustics. To take full advantage of the relatively easy accessed unlabeled data, this paper proposes a framework for underwater acoustic source localization based on a two-step semi-supervised learning classification model. The first step is trained in unsupervised mode with the whole available dataset (labeled and unlabeled dataset), and it consists of a convolutional autoencoder (CAE) for feature extraction and self-attention (RA) mechanism for picking more useful features by applying constraints on the CAE. The second step is trained in supervised mode with the labeled dataset, and it consists of a multilayer perceptron connected to an encoder from the first step and is used to perform the source location task. The proposed framework is validated on uniform vertical line array data of SWellEx-96 event S5. Compared with the supervised model and the model without the RA, the proposed framework maintains good localization performance with the reduced labeled dataset, and the proposed framework is more robust when the training dataset and the test dataset of the second step are distributed differently, which is called “data mismatch.”

Shallow-water waveguide acoustic analysis in a fluctuating environment.

The Acoustic Laboratory for Marine Applications (ALMA) is a deployable and autonomous acoustic system, designed by DGA Naval Systems, to address problems in underwater acoustics, such as sound propagation in fluctuating environments. In this article, data from the ALMA-2016 at-sea campaign are used to analyze the ocean fluctuation's influence on sound propagation in a shallow-water waveguide. The experiment took place on the continental shelf of the island of Corsica in November 2016. A source and a receiver array were 9.3 km apart in a nearly constant water depth of 100 m. The source emitted a variety of signals from which the chirp (1-13 kHz) is used to extract the waveguide eigenrays. To do so, a time-domain beamforming is performed on the match-filtered received signals with an automatic detection of local maxima in the time of arrival/direction of arrival (TOA/DOA) domain. A 2 min acquisition period of more than 13 h duration shows significant fluctuations in eigenray TOAs/DOAs. Qualitative comparisons with synthetic signals obtained from simulations in two and three dimensions permit reproduction of the observed eigenray fluctuations without including range dependence of the sound-speed profile.

A generalization of WKBJ method for solving a system describing propagation of coupled modes in underwater acoustics

A generalization of the WKBJ method for the case of coupled modes propagation in problems of underwater acoustics is proposed. The generalized WKBJ ansatz has the form of a product of a matrix of phase factors and vector of amplitudes. The transport equation in the matrix form is derived, and the phase factors are computed exactly. The similarities and differences with other forms of mode coupling equations are discussed. A numerical example demonstrating the accuracy of the generalized WKBJ solution is presented.

A finite element method with cover functions for underwater acoustic propagation problems

To improve the performance of the classical finite element method (FEM) in acoustic analyses, in this work the interpolation cover functions are incorporated into the standard linear nodal shape functions to give the finite element with cover functions (FEwC). Since the original linear approximation space of the standard FEM is significantly enriched, more accurate solutions can be obtained. However, this FEwC encounters the intractable linear dependence (LD) problem. In this paper the LD problem is studied systematically and then an effective method is given to completely avoid this problem. The imposition of Dirichlet boundary conditions in FEwC which is different from that in FEM is also illustrated. Besides, to handle the acoustic scattering problems in the unbounded domains, the Dirichlet-to-Neumann (DtN) map technique is employed as the non-reflecting boundary condition. The dispersion error analyses and several typical numerical experiments associated with the joint frequency-domain and time-domain analysis are performed to exhibit the high performance of the FEwC compared with the classic FEM and the edge-based smoothed FEM in solving underwater acoustic propagation problems.

Cell-Based Smoothed Radial Point Interpolation Method for Underwater Acoustic Scattering Problems

In this work, a novel cell-based smoothed radial point interpolation method (CSRPIM) is used to deal with underwater acoustic scattering problem. The nature of infinite domain has been changed by exact Dirichlet-to-Neumann (DtN) boundary condition. Owing to the use of gradient smoothing operation and consequent properly softened stiffness, the CSRPIM model is capable of reducing pollution error significantly. A theoretical analysis is carried out to elucidate the superiority in controlling pollution error of CSRPIM. By selecting virtual nodes properly in condensed shape functions, the interpolation error in the CSRPIM can also be reduced. Through several simple acoustic scattering numerical examples, advantages of CSRPIM have been verified. The results show that the CSRPIM can be applied directly to solve acoustic scattering problems and can obtain higher accuracy than traditional linear finite element method (FEM) even if in relatively high frequency range. Also, the use of linear triangular elements in the present model makes the analysis of practical underwater acoustic problems with complex shapes easier.

Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods.

EVOLUTION OF LINEAR WAVES IN A POROUS MEDIUM SATURATED WITH A BUBBLE LIQUID

Background The study of the processes of propagation of pressure waves in liquid-saturated porous media is associated not only with solving practical problems of underwater acoustics, seismics, object protection, non-destructive testing, etc., but it is also necessary to understand the fundamental foundations of wave processes. in such environments. Aims and Objectives Investigation of the evolution of a pulse of finite duration in a porous medium saturated with a gas-liquid mixture. Analysis of two situations of the location of bubbles in a porous medium. The first, when the bubble envelopes several pores, and the second situation - gas bubbles are much smaller than the pore sizes and are located on the pore walls. Results The dynamics of linear waves in a porous medium filled with a gas-liquid mixture has been studied. The influence of the determining factors of a bubbly liquid (volumetric content, dispersion of bubbles) and a porous medium on the shape and decay of impulse disturbances is analyzed.

Inversion in rough surface scattering via the frequency-difference autoproduct

The forward scattering of acoustic waves, with wavenumber k, from a rough surface is a well-known problem in underwater acoustics. A random rough surface, statistically characterized by its root-mean-square roughness h and its correlation length L, exponentially reduces reflected field coherence as kh increases. The frequency-difference autoproduct is a nonlinear mathematical construction that recovers field coherence in rough surface scattering by down shifting recorded signal frequencies, thereby minimizing apparent surface roughness. Salient features of frequency-difference-autoproduct forward-scattering results, including the dependence on both h and L, are shown. Further, this presentation describes the unique capability afforded by the frequency-difference autoproduct to approach the inverse problem in a rough surface scattering environment. In particular, inference of a random rough surface's root-mean-square height and correlation length is accomplished via autoproduct-based signal processing methods. The work implements an isotropic surface roughness profile corresponding to in-band roughness values of 1.25 < kh < 3.5. Using the same scattered signals, conventional in-band inversion techniques are compared to the results obtained via the frequency-difference autoproduct. Experimental results from a laboratory water tank may be discussed as well. [Work supported byONR.]

Performance weighted blended spectral estimation on experimental seaglider data

A common problem in underwater acoustics is the non-parametric estimation of a power spectrum from time series data with windowed Fourier transforms. If characteristics of the environment such as signal to noise ratio or the frequency of loud line components are known then an appropriate window can be identified. When estimation is performed without a priori knowledge of the environment, an analyst often uses an ensemble of different windowed Fourier transforms and synthesizes the results. When no analyst is available this process must be automated. The performance weighted blended~(PWB) spectrum estimator automates the work of an analyst by weighting each estimator in an ensemble based on its performance and summing them to create a new estimator that performs as well or better than each estimator in the ensemble regardless of the characteristics of the environment [Tucker et al., IEEE UASP Workshop (2019)]. This talk summarizes the PWB estimator, and compares it to existing estimators when applied to passive acoustic data collected from a hydrophone mounted on a Seaglider autonomous underwater vehicle. Additionally, the talk presents modifications to the original PWB estimator that offer improved robustness in the presence of loud transient signals, e.g., noise from glider hardware. [Work supported by ONR.]

Differences in regression, classification, and multi-task deep learning on pressure time series for range and seabed type

Estimating source-receiver range and predicting seabed type are important problems in underwater acoustics. Deep learning solutions for simultaneous predictions have been previously shown some success for these problems due to their ability to learn patterns from large datasets. However, one important question to consider in deep learning is how predictions should be made: A network can be configured to classify or regress predictions. In this study, that question is explored by comparing predictions from networks trained on simulated SUS charge pressure time-series configured to either regress or classify range and seabed predictions from measured data taken with the IVAR system during the 2017 Seabed Characterization Experiment. To further this inquiry, networks configured to regress range and classify seabed type (via “multi-task” learning) are also explored. Separating the two predictions proves to be useful, as the networks using multi-task learning perform better at predicting range and seabed class simultaneously than those configured only for classification or regression. The results of this experiment illustrate the need to use the proper type of network outputs depending on the desired predictions. [Work supported by the Office of Naval Research.]

Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.

A new methodology in the study of acoustic fields in the almost stratified ocean

In this work, we present the mathematical expressions for the study of the behaviour of the acoustic field in almost stratified ocean. These expressions are suitable for numerical simulation and are derived from the spectral parameter power series method applied in problems of underwater acoustics. Analytical treatment for the obtention of the dispersion equation and normed eigenfunctions of the problem is given.

Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

Acoustic seafloor classification using machine learning and simulations of Helmholtz equations

We discuss numerical methods for inverse problems in high frequency underwater acoustics, aiming to recover detailed characteristics of the seafloor from measured backscatter data generated from SONAR systems. The key to successful inversion is the use of accurate forward modeling capturing the dependence of backscatter on seafloor properties, such as sediment type, roughness, and thickness. Prediction models are often statistical models formed using years of collected ground-truth data or physical models that require costly simulations or unrealistic environmental assumptions. Sophisticated tools are needed to accommodate complicated scenarios, i.e., uncharted seafloor landscapes. To enable a rapid, remote, and accurate seafloor parameter recovery, we propose a combination of machine learning and high fidelity forward modeling and simulation of the physical wave propagation and scattering process. The idea is to partition environments on the order of kilometers in width into much smaller “template” domains, a few meters in width, where the sediment layer can be described using a few parameters. Machine learning is used to train a classifier using a reference library of simulations of Helmholtz equations on the domains. We investigate the potential of multilayer perceptrons and generalized regression in sediment identification using the reference library. [Work supported by NSF and ONR.]

Application of Smoothed Finite Element Method to Two-Dimensional Exterior Problems of Acoustic Radiation

In this work, the smoothed finite element method using four-node quadrilateral elements (SFEM-Q4) is employed to resolve underwater acoustic radiation problems. The SFEM-Q4 can be regarded as a combination of the standard finite element method (FEM) and the gradient smoothing technique (GST) from the meshfree methods. In the SFEM-Q4, only the values of shape functions (not the derivatives) at the quadrature points are needed and the traditional requirement of coordinate transformation procedure is not necessary to implement the numerical integration. Consequently, no additional degrees of freedom are required as compared with the original FEM. In addition, the original “overly-stiff” FEM model for acoustic problems (governed by the Helmholtz equation) is properly softened due to the gradient smoothing operations implemented over the smoothing domains and the present SFEM-Q4 possesses a relatively appropriate stiffness of the continuous system. Therefore, the well-known numerical dispersion error for Helmholtz equation is decreased significantly and very accurate numerical solutions can be obtained by using relatively coarse meshes. In order to truncate the unbounded domains and employ the domain-based numerical method to tackle the acoustic radiation in unbounded domains, the Dirichlet-to-Neumann (DtN) map is used to ensure that there are no spurious reflections from the far field. The numerical results from several numerical examples demonstrate that the present SFEM-Q4 is quite effective to handle acoustic radiation problems and can produce more accurate numerical results than the standard FEM.

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

AbstractA volunteer computing project aimed at solving computationally hard inverse problems in underwater acoustics is described. This project was used to study the possibilities of the sound speed profile reconstruction in a shallow-water waveguide using a dispersion-based geoacoustic inversion scheme. The computational capabilities provided by the project allowed us to investigate the accuracy of the inversion for different mesh sizes of the sound speed profile discretization grid. This problem suits well for volunteer computing because it can be easily decomposed into independent simpler subproblems.

Edwin Carstensen’s contributions to early research at the Navy’s Underwater Sound Reference Laboratory

Edwin Carstensen enrolled at the Case School of Applied Science to study acoustics under Dayton Miller but upon Miller’s passing in 1941, he took up study under Robert Shankland. The influence of World War II reached deeply into academia in those years and Carstensen’s advisor Shankland became the Director of Underwater Acoustic Metrology at Columbia University—Division of War Research (DWR). Thus Carstensen was first introduced to practical problems in underwater acoustics of critical importance to the Navy as a graduate student and employee (of DWR). At that time, the Underwater Acoustic Metrology center was headquartered in New York City at the Empire State building but established a remote calibration and research facility near Orlando, Florida, which subsequently became the Navy’s Underwater Sound Research Laboratory (USRL). Carstensen began his life-long study of propagation of sound in bubbly media and the effects of cavitation. He also worked on diffraction and calibration of hydrophones by self-reciprocity, the subject of his Master’s thesis that was delayed until 1947 due to his war-effort service. This paper summarizes some of his early work, publications and collaborations in the Underwater Acoustics.

Multi-frequency sparse Bayesian learning for matched field processing

Compressive sensing has been applied to underwater acoustic problems. Using multi-frequency sparse Bayesian learning (SBL), we present simulation and data results (SWellEx-96 Event S5) including mismatch. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. Results for a multiple-source scenario indicate that SBL outperforms WNC and MUSIC when localizing a quiet source in the presence of a stronger source. Furthermore, simulations (including snapshots not corresponding exactly to replicas) and data results demonstrate that SBL offers robustness to mismatch including array-tilt. The array-tilt mismatch in the data varies over time and is especially pronounced at the closest point of approach, being 2 degrees. Because of its computational efficiency and performance, SBL is practical for real time applications requiring an adaptive and robust processor.