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Frequency-difference matched-field processing is a high-frequency source localization technique formulated by matching the frequency-difference autoproduct of the measured field and replicas at the difference-frequency. Although it successfully localizes sound sources by sparse vertical array in shallow or deep ocean with an environmental mismatch, there is still some ambiguity in replica modeling and signal processing. Here, the existing conventional processor is modified to match the bandwidth-averaged autoproduct of the measured field with replicas of the bandwidth-averaged autoproduct, or approximately its self-term for the expected source locations. The proposed processor is consistent with the perspective of matched-field processing and can naturally relieve some drawbacks of the existing one, such as low peak or low dynamic range on the ambiguity surface. Numerical tests are carried out in several shallow ocean environments and the source localization using experimental data are performed to confirm the properties of the proposed processor. It is found that the high-frequency diffracted field always leaves traces on its bandwidth-averaged autoproduct field. These high-frequency marks cause a bias in source localization in the presence of a sound speed mismatch even in low difference-frequencies. [ABSTRACT FROM AUTHOR]

A ray approach is used to simulate subcritical scattering from a buried target at low-to-high frequencies (100 Hz–15 kHz). A penetrating wave at a subcritical angle decays along the depth at the bottom (i.e., evanescent wave) and propagates horizontally at a subcritical angle-dependent speed lower than the sound speed of the bottom. The corresponding target strength (TS) is distinguished from that of a standard plane wave. Its pattern is asymmetric by the evanescent wave including for symmetric targets and is more complicated owing to the higher wavenumber induced by the lower speed of the evanescent wave. A scattered signal is simulated by considering the features of the penetrating wave with the TS and then verified using the finite element method. In the ray approach, once the TS is computed, a scattered field is efficiently derived with low computational complexity. Strong peaks are observed in the scattered signal via mid-frequency enhancement; however, their amplitudes are less than those yielded by the free-field target owing to the more diminished penetrating waves at higher frequencies. The peaks indicate the possibility of detecting the buried target using a receiver near the target (bistatic sonar) with a broadband source signal that includes low-to-mid frequencies. [ABSTRACT FROM AUTHOR]

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results. [ABSTRACT FROM AUTHOR]

Featured Application: Sonar system. In this paper, a depth-bistatic bottom reverberation model that employs the ray theory is presented. The model can be applied to an active towed array in the ocean. The reverberation time series are modeled under the depth-bistatic assumption and their Doppler shift is calculated based on the actual source–receiver geometry. This model can handle N × 2D range-dependent bathymetry, the geometry of a triplet array, and the Doppler motion of the source, targets, and receiver. The model predictions are compared with the mid-frequency reverberation data measured by an active triplet towed array during August 2015 in the East Sea, Korea. These data are collected with a variable depth source at mid-frequency and the triplet line array in a deep-water environment. Model predictions of the beam time series and its spectrogram are in good agreement with the measurement. In particular, we discuss the effects of the source and receiver depths on the reverberation in deep water observed in both the measured and modeled results. [ABSTRACT FROM AUTHOR]

Noise induced by incipient-propeller tip vortex cavitation (TVC) has a few sources near the propeller tips, which radiate a broadband signal. This article describes a compressive sensing (CS)-based TVC localization technique for coherent multiple-frequency processing, which jointly processes the measured data at multiple frequencies. Block-sparse CS, which groups several single-frequency measurements into blocks, is adopted for coherent multiple-frequency processing. The coherent multiple-frequency processing improves localization performance over that of single-frequency processing. Unlike single-frequency processing using conventional CS, which combines independent single-frequency measurement treatments by averaging, coherent multiple-frequency processing produces accurate localization without requiring a sufficient number of treated frequencies, long-time-sampled data with a time-invariant signal assumption, or even a single cavitation event. The approach is demonstrated on experimental data from a transducer source experiment and a cavitation source experiment. [ABSTRACT FROM AUTHOR]

In this study, the three-dimensional Cartesian acoustic parabolic equation is fully solved in the stair-step representation of an ocean environment based on the split-step Padé algorithm. The sum representation of the Padé approximation of a full exponential operator in the split-step marching solution is used for parallel computing. A stabilized self-starter solution is derived in the same way. This solver is benchmarked against reference solutions for a rectangular duct propagation, an ideal wedge problem, and an axisymmetric ocean. Additionally, a solution for the Gaussian canyon problem is provided and compared with that obtained from the approximate parabolic equation solver, considering a leading-order cross-term. [ABSTRACT FROM AUTHOR]

An approximate form of three-dimensional Cartesian split-step marching solution for the acoustic parabolic equation is derived in order to obtain the efficient algorithm for sound propagation in the three-dimensional ocean. The operator splitting method is used to split the full exponential operator into three exponential operators for depth, cross-range, and the combination of the two. The first two terms are implemented with the split-step Padé algorithm and the final term is implemented with the Taylor series expansion in depth and cross-range operator. In order to resolve the divergence of Taylor approximation out of the interval of convergence, the rational filter of rectangular type is applied to the depth and cross-range operator. The use of the filter improves the stability of the solution but requires extra numerical burdens. Numerical issues involving the accuracy, efficiency, and stability of the proposed model are discussed and illustrated in an ocean wedge environment. [ABSTRACT FROM AUTHOR]

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation. [ABSTRACT FROM AUTHOR]

In this paper, a ray-based noise model is developed to calculate the two-point spatial coherence function for ocean-surface-generated noise in a hydrophone triplet, which consists of three hydrophones arranged as an equilateral triangle with side length $d$. While existing ray-based models employ the semianalytic approach, this model is more generally formulated for the 3-D noise field with vertical and azimuthal directivity using a ray-tracing model. Moreover, we derive a Maclaurin series of the formulated spatial coherence function for the ratio of the hydrophone spacing and the acoustic wavelength $\lambda $. This series explicitly reveals the feature of spatial coherence for the noise field as a function of the orientation of the hydrophone pair and the ocean environmental coefficients. At small $d/\lambda $ , the leading-order terms alone provide a good approximate solution for the spatial coherence function, which enables rapid computation of six coherence functions for the hydrophone triplet. Especially, for the ocean environment with weakly azimuth-dependent ocean bathymetry, the contribution from each term in the derived series is analyzed and a simpler formula for the spatial coherence function is suggested. [ABSTRACT FROM AUTHOR]

An efficient algorithm for long-range monostatic reverberation is developed using geometrical ray-bundle as its propagation model. In the standard approach for reverberation modeling, scattered signals are numerically integrated along the rough surface. In this work, the integration along the rough surface is converted to an integration along the delay time of source signal. It can be expressed in an analytic form under the assumption of ignoring time-dispersion effect. Repetitive eigen-ray searches for calculating scattered signals are avoided by using a split-step marching algorithm for the range based on the geometrical ray-bundle. When the bottom is range-dependent, arrival times of scattered signals from the rough bottom at same range is different according to azimuthal angles and they cannot be directly summed in time domain. The reverberation signals along the azimuthal angles are interpolated to obtain new reverberation signals at certain times. They are summed to compute a time series reverberation signal from the rough sloping bottom. The algorithm is applied to problems of reverberation modeling workshop, and its results are compared with those from the standard reverberation algorithm. [ABSTRACT FROM AUTHOR]

The classical ray approach for monostatic ocean boundary reverberation has been re-examined based on a geometrical ray-bundle concept. In this new formulation, the impulse response for the averaged scattering intensity is expressed by a simple function consisting of continuous ray-bundle quantities with respect to the time, and which can be regarded as a generalized function for ray-based reverberation in a boundary cell. To numerically evaluate this impulse response, a zeroth- and a first-order polynomial interpolation method are respectively applied to approximate the ray-bundle quantities. Then, the impulse function is reduced to the forms of the delta function and the rectangular function either with or without linear amplitude modulation linear in time. These basic functions are used to develop an explicit numerical scheme for the monostatic reverberation, which originates in the split-step marching algorithm for the range. This numerical scheme provides a considerable accuracy even with larger range steps and gives reasonable results for numerical examples of ocean waveguides with isovelocity, summer, and winter sound-speed profiles. [ABSTRACT FROM AUTHOR]

This study was undertaken to investigate the types and concentrations of microbial agents in various medical wastes as well as to characterize their survivals in these wastes at different temperatures for microbial risk assessment. Medical wastes collected from 5 major hospitals in South Korea were classified and stored at three different temperatures (-20, 6, and 30°C). Presence of various microorganisms such as pathogenic viruses and bacteria were investigated by both cultivation and by (RT)-PCR assays. A number of (opportunistic) pathogenic bacteria, including Pseudomonas spp., Lactobacillus spp., Staphylococcus spp., Micrococcus spp., Kocuria spp., Brevibacillus spp., Microbacterium oxydans, and Propionibacterium acnes, were identified from the various medical wastes. In addition, pathogenic viruses such as noroviruses and hepatitis B virus were also detected in one of the human tissue wastes. Commonly identified bacterial and viral pathogens such as Pseudomonas spp., Corynebacterium diphtheriae, Escherichia coli, Staphylococcus spp., and respiratory synctial virus (RSV) were inoculated into either gauzes or diapers, and their survivals were characterized. Viral agents such as RSV showed poor survival in most environmental conditions, and demonstrated that various pathogens could be present in medical wastes but that the associated health risk appeared to be low. However, medical waste should be carefully controlled and monitored to prevent nosocomial infection associated with the exposure to these wastes. [ABSTRACT FROM AUTHOR]

This paper describes the results of range-dependent geoacoustic inversion using vertical line array data obtained from the 4th Matched Acoustic Properties and Localization Experiment conducted in the East Sea of Korea. The narrowband multitone continuous-wave signal from the towed source was analyzed to estimate the range-dependent geoacoustic properties along the radial track. The primary approach is based on the sectorwise inversion scheme. The inversion region up to 7.5 km from the vertical line array was divided into several segments, and the subinversions for each segment were performed sequentially. To reduce the dominance of low-angle arrivals, which bears little information for the bottom segment in question, matched beam processing with beam filtering was used for the cost function. The performance of proposed algorithm was tested using simulated data for an environment representative of the experimental site. The inversion results for the experimental data were consistent with the geophysical database and were validated from matched-field source localization using frequencies different from those used in the inversion. [ABSTRACT FROM AUTHOR]

We observed a distinct drop-off region in the bearing-time record of acoustic reverberation data acquired from the south-western continental margin of the Ulleung Basin, East Sea, in the summer of 2015. 3 kHz continuous waves with pulse lengths of 0.1, 0.3, and 1.0 s were used as source pulses, with an R/V Cheonghae vessel towing a variable depth source and a triplet towed array toward the deep sea from shallow water. The observed pattern changed as the R/V Cheonghae moved across the continental slope further into the sea. This pattern arises as a result of the downward-refracted beams in the 1/2 convergence zone interacting with the soft bottom. In addition, the boundary of the drop-off region was modeled with the two-way maximum travel time of the first bottom-reflected rays using the bathymetry model of the General Bathymetric Chart of the Oceans, 2020. Some discrepancies were observed when comparing the modeled curve to the measured results, and the inaccuracy of the bathymetry model on the continental slope could be the main cause of these discrepancies. This pattern could be useful for bathymetry mapping, as well as estimations of source and receiver configurations. [ABSTRACT FROM AUTHOR]

Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l 1 -norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity. [ABSTRACT FROM AUTHOR]

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean. [ABSTRACT FROM AUTHOR]