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Mid-frequency transmissions (1.5 to 7.5 kHz) from a towed source to a drifting array one convergence zone away demonstrate smoothly varying phase. Calculations of the strength and diffraction parameters Φ and Λ for a representative background environment predict partially saturated propagation. For a single convergence zone path, the evolving phase rate due to the changing internal wave field corresponds to a narrowband coherent integration time, defined by the length of a discrete Fourier transform that likely captures the signal, from ∼370 s at 10 kHz to ∼1370 s at 1 kHz in the absence of source motion. Simulated propagation with a split-step parabolic equation solver through a sound speed field with thermocline fine structure and a Garrett-Munk internal wave displacement field provides a full wave picture that is consistent with the statistical predictions. Experimental data from the Philippine Sea demonstrates signal phase that is highly correlated with source tow body vertical excursions, measured by a pressure sensor, that couple into horizontal motion. The received signal can be integrated for up to 128 s at 5.5 kHz, with the upper limit thought to be due to non-uniform source motion rather than ocean dynamics. [ABSTRACT FROM AUTHOR]

The very low-frequency noise from merchant ships provides a good broadband sound source to study the deep layers of the seabed. The nested striations that characterize ship time-frequency spectrograms contain unique acoustic features corresponding to where the waveguide invariant β becomes infinite. In this dataset, these features occur at frequencies between 20 and 80 Hz, where pairs of modal group velocities become equal. The goal of this study is to identify these β = ∞ frequencies in ship noise spectrograms and use them to perform statistical inference for the deep layer sound speeds and thicknesses in the New England Mudpatch for a larger number of ships and acoustic arrays over a larger geographical region than previously studied. Marginal probability distributions of the data indicate that using singular points for a feature-based inversion yields an estimate of the sound speed and a limiting value for the thickness of the first deep layer. Heterogeneity is examined by correlating spatial variability of the deep layer sound speeds with ship tracks. [ABSTRACT FROM AUTHOR]

Ray-tracing is typically used to estimate the depth and range of an acoustic source in refractive deep-water environments by exploiting multipath information on a vertical array. However, mismatched array inclination and uncertain environmental features can produce imprecise trajectories when ray-tracing sequences of individual acoustic events. “Double-difference” methods have previously been developed to determine fine-scale relative locations of earthquakes along a fault [Waldhauser and Ellsworth (2000). Bull. Seismolog. Soc. Am. 90, 1353–1368]. This technique translates differences in travel times between nearby seismic events, recorded at multiple widely separated stations, into precise relative displacements. Here, this method for acoustic multipath measurements on a single vertical array of hydrophones is reformulated. Changes over time in both the elevation angles and the relative arrival times of the multipath are converted into relative changes in source position. This approach is tested on data recorded on a 128-element vertical array deployed in 4 km deep water. The trajectory of a controlled towed acoustic source was accurately reproduced to within a few meters at nearly 50 km range. The positional errors of the double-difference approach for both the towed source and an opportunistically detected sperm whale are an order of magnitude lower than those produced from ray-tracing individual events.

The multi-snapshot, multi-frequency sparse Bayesian learning (SBL) processor is derived and its performance compared to the Bartlett, minimum variance distortionless response, and white noise constraint processors for the matched field processing application. The two-source model and data scenario of interest includes realistic mismatch implemented in the form of array tilt and data snapshots not exactly corresponding to the range-depth grid of the replica vectors. Results demonstrate that SBL behaves similar to an adaptive processor when localizing a weaker source in the presence of a stronger source, is robust to mismatch, and exhibits improved localization performance when compared to the other processors. Unlike the basis or matching pursuit methods, SBL automatically determines sparsity and its solution can be interpreted as an ambiguity surface. Because of its computational efficiency and performance, SBL is practical for applications requiring adaptive and robust processing.

The coherent recombination of a direct and seabed reflected path is sensitive to the geophysical properties of the seabed. The concept of feature-based inversion is used in the analysis of acoustic data collected on a vertical line array (VLA) on the New England continental shelf break in about 200 m of water. The analysis approach for the measurements is based on a ray approach in which a direct and bottom reflected path is recombined, resulting in constructive and destructive interference of the acoustic amplitudes with frequency. The acoustic features have the form of prominent nulls of the measured received levels as a function of frequency as a broadband (500–4500 Hz) source passes the closest point of approach to the VLA. The viscous grain shearing (VGS) model is employed to parameterize a two-layer seabed model. The most likely seabed is a sand sediment with a porosity of about 0.42. There is a possibility of a thin (less than 0.5 m) surface layer having a slightly higher porosity between 0.45 and 0.50. Using the estimates for the VGS parameters inferred from the short-range frequency features, a normal mode model is used to predict the received acoustic levels over larger range scales. [ABSTRACT FROM AUTHOR]

Large surface wave breaking events in deep water are acoustically detectable by beamforming at 5–6 kHz with a mid-frequency planar array located 130 m below the surface. Due to the array's depth and modest 1 m horizontal aperture, wave breaking events cannot be tracked accurately by beamforming alone. Their trajectories are estimated instead by splitting the array into sub-arrays, beamforming each sub-array toward the source, and computing the temporal cross-correlation of the sub-array beams. Source tracks estimated from sub-array cross-correlations match the trajectories of breaking waves that are visible in aerial images of the ocean surface above the array. [ABSTRACT FROM AUTHOR]

An ocean acoustics experiment in 2017 near a shipping lane on the New England continental shelf in about 75 m of water provided an opportunity to evaluate a methodology to extract source signatures of merchant ships in a bottom-limited environment. The data of interest are the received acoustic levels during approximately 20 min time intervals centered at the closest position of approach (CPA) time for each channel on two 16-element vertical line arrays. At the CPA ranges, the received levels exhibit a frequency-dependent peak and null structure, which possesses information about the geophysical properties of the seabed, such as the porosity and sediment thickness, and the characterization of the source, such as an effective source depth. The modeled seabed is represented by two sediment layers, parameterized with the viscous grain shearing (VGS) model, which satisfies causality, over a fixed deep layered structure. Inferred estimates of the implicit source levels require averaging an error function over the full 20 min time intervals. Within the 200–700 Hz band, the Wales–Heitmeyer model captures the inferred frequency dependence of the source levels. [ABSTRACT FROM AUTHOR]

This paper presents inversion results for three datasets collected on three spatially separated mud depocenters (hereafter called mud ponds) during the 2022 Seabed Characterization Experiment (SBCEX). The data considered here represent modal time-frequency (TF) dispersion as estimated from a single hydrophone. Inversion is performed using a trans-dimensional (trans-D) Bayesian inference method that jointly estimates water-column and seabed properties along with associated uncertainties. This enables successful estimation of the seafloor properties, consistent with in situ acoustic core measurements, even when the water column is dynamical and mostly unknown. A quantitative analysis is performed to (1) compare results with previous modal TF trans-D studies for one mud pond but under different oceanographic condition, and (2) inter-compare the new SBCEX22 results for the three mud ponds. Overall, the estimated mud geoacoustic properties show no significant temporal variability. Further, no significant spatial variability is found between two of the mud ponds while the estimated geoacoustic properties of the third are different. Two hypotheses, considered to be equally likely, are explored to explain this apparent spatial variability: it may be the result of actual differences in the mud properties, or the mud properties may be similar but the inversion results are driven by difference in data information content. [ABSTRACT FROM AUTHOR]

[1] This paper addresses the problem of estimating the lower atmospheric refractivity ( M profile) under nonstandard propagation conditions frequently encountered in low-altitude maritime radar applications. This is done by statistically estimating the duct strength (range- and height-dependent atmospheric index of refraction) from the sea surface reflected radar clutter. These environmental statistics can then be used to predict the radar performance. In previous work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate the atmospheric refractivity from returned radar clutter. Although GA is fast and estimates the maximum a posteriori ( MAP) solution well, it poorly calculates the multidimensional integrals required to obtain the means, variances, and underlying posterior probability distribution functions of the estimated parameters. More accurate distributions and integral calculations can be obtained using MCMC samplers, such as the Metropolis-Hastings and Gibbs sampling (GS) algorithms. Their drawback is that they require a large number of samples relative to the global optimization techniques such as GA and become impractical with an increasing number of unknowns. A hybrid GA-MCMC method based on the nearest neighborhood algorithm is implemented in this paper. It is an improved GA method which improves integral calculation accuracy through hybridization with a MCMC sampler. Since the number of forward models is determined by GA, it requires fewer forward model samples than a MCMC, enabling inversion of atmospheric models with a larger number of unknowns.

During an experiment in deep water off the coast of Southern California, wind speeds ranged from 10 to 15 m/s and wind forcing produced large breaking waves. A mid-frequency vertical planar hydrophone array recorded underwater ambient noise while an airplane equipped with a high-resolution video camera captured images of the sea surface above the array. Beams of ambient noise between 5 and 6 kHz were projected onto the sea surface and synchronized in space and time with the aerial images. Despite the array's limited azimuthal resolution of the surface, due to its modest 1 m horizontal aperture and relatively deep 130 m deployment depth, concentrated areas of high intensity in the acoustic surface projection were observed to match visible breaking events in the aerial images. [ABSTRACT FROM AUTHOR]

Vector acoustic properties of a narrowband acoustic field are observed as a function of range from a source towed in waters of depth 77 m on the New England Mud Patch. At the source frequency (43 Hz), the waveguide supported three trapped modes, with mode 2 weakly excited owing to the towed source depth. The receiving sensor was positioned 1.45 m above the seafloor with a sampling range aperture of 2500 m. The vector acoustics observations enabled study of vortex regions that encompass two singular points for active acoustic intensity: the vortex point, which is co-located with a dislocation, and stagnation point. Interpretative modeling, based on the normal modes and using a geoacoustic model consistent with those emerging from studies conducted at this location, is in agreement with these measurements. Model-data comparisons were based on the first-order variables of acoustic pressure and velocity along with inverse Hankel transforms, which yield normalized horizontal wavenumber spectra, and second-order variables in the form of horizontal and vertical intensity as well as non-dimensional intensity-based ratios. These measures provide a degree of observational confirmation of some vortex region properties. Both observations and modeling point to a gradual deepening of such regions with increasing range owing to sediment attenuation. [ABSTRACT FROM AUTHOR]

An at-sea experiment in deep water was conducted to explore the impact of small-scale sound-speed variability on mid-frequency (1 – 10 kHz) acoustic propagation. Short-range (1 – 5 km) acoustic transmissions were sent through the upper ocean (0 – 200 m) while oceanographic instruments simultaneously measured the ocean environment within 2 km of the single upper turning points of the acoustic transmissions. During these transmissions, acoustic receptions over a 7.875 m vertical line array show closely spaced, sometimes interfering arrivals. Ray and full-wave simulations of the transmissions using nearby sound-speed profiles are compared deterministically to the received acoustic signals. The sensitivity of the acoustic arrivals to the vertical scales of ocean sound speed is tested by comparing the observed and simulated arrival intensity where the sound-speed profile used by the simulation is smoothed to varying scales. Observations and modeling both suggest that vertical fine-scale structures (1 – 10 m) embedded in the sound-speed profile have strong second derivatives which allow for the formation of acoustic caustics as well as potentially interfering acoustic propagation multipaths. [ABSTRACT FROM AUTHOR]

Multipath arrivals are identified and extracted from midfrequency impulse responses at a vertical line array produced by a broadband source transmitting linearly frequency modulated pulses in the SBCEX 17 experiment. Arrival times are extracted from received data using particle filtering and smoothing. Specifically, sequential filtering is applied to the received time series at sixteen phones for the estimation of three paths generated by sound interacting with the propagation medium. This is followed up with a smoother that provides “crisp” arrival time probability densities. A peak-finding method is used to identify a fourth path. The estimated arrival times obtained by the process above are linked with a sound propagation model based on ray tracing and linearization for estimation of source location and water column depth and sound speed. An exhaustive search follows for inversion for sediment sound speed and thickness. Because the source is moving, receptions are available on a track and arrival time estimation and corresponding inversion are performed at every source location within the track. The unknown parameters are estimated sequentially with two approaches. Approach one is linearization using prior information from one location propagated to the next. Approach two relies on a second particle filter, capturing the dynamic evolution of parameters along the track. Both techniques lead to sediment property estimation. Results from the two methods agree, pointing to robustness in inversion when impulse responses and arrival times estimated from those are employed as input to the inverse problem. [ABSTRACT FROM AUTHOR]

The estimation of ship source levels (SSLs) in shallow-water environments can be complicated by sound interaction with the seabed. Uncertainty in seabed properties influences SSL estimates, and it is of interest to mitigate and quantify such effects. This article proposes a probabilistic approach to ship radiated noise recorded on a vertical line array (VLA) of hydrophones to infer SSL and properties of a mud-sand shallow water seabed on the New England Shelf. The approach, trans-dimensional Bayesian marginalization, samples probabilistically over complex spectral source strengths, source depths/ranges, and number of seabed layers and geoacoustic parameters of each layer. The Bayesian information criterion is applied to determine the appropriate number of (point) sources used to describe a ship. Radiated noise due to two merchant ships passing the VLA at beam aspect at 3.2−3.4-km range is considered. The SSL estimates agree well with reference spectra from shallow-water studies on large ensembles of merchant ships. The average SSL uncertainty (in terms of one-half the interquartile range interval) is 3.2 dB/Hz for low-frequency narrowband (20−120 Hz) and 1.8 dB/Hz for broadband noise (190−590 Hz). Seabed layering and geoacoustic parameter estimates agree reasonably well with mud-over-sand seabed models from other inversions in the area. [ABSTRACT FROM AUTHOR]

Acoustic recordings of signals in the 1.5–4.0-kHz band were analyzed for information about the sound speed and attenuation frequency dispersion of a fine-grained sediment found in the New England Mudpatch. Analysis of piston cores established prior bounds for a geophysical parameterization of a seabed model that predicts Kramers–Kronig dispersion relations. Sediment layers are described by the Buckingham viscous grain shearing (VGS) model that accounts for the effects of overburden pressure of compressional and shear speeds and attenuations. A statistical inverse problem was solved by using multiple samples of received levels recorded on two vertical line arrays as a function time and hydrophone depth for six frequencies in the 1.5–4.0-kHz band. A statistical inference model that assumed both model parameters and data samples are random variables quantified information content from marginalization of a conditional posterior probability distribution for the geophysical parameters that characterize the mud layer. From the inferred geophysical parameter point estimates the sediment sound speed and attenuation frequency dispersion are predicted and compared to previously reported direct measurements. Also, the predicted sound-speed gradient in the mud sediment from the VGS model is compared to a previous inference that utilized explosive sources. [ABSTRACT FROM AUTHOR]

We address the problem of tracking the spatial and temporal lower atmospheric variations in maritime environments. The evolution of the range and height-dependent index of refraction is tracked using the sea clutter return from sea-borne radars operating in the region. A split-step fast Fourier transform based parabolic equation approximation to the wave equation is used to compute the clutter return in complex environments with varying index of refraction. In addition, regional statistics are incorporated as prior densities, resulting in a highly nonlinear and non-Gaussian tracking problem. Tracking algorithms such as the extended Kalman, unscented Kalman and particle filters are used for tracking both evaporative and surface-based electromagnetic ducts frequently encountered in marine environments. The tracking performances and applicability of these techniques to different types of refractivity-from-clutter problems are studied using the posterior Cramer-Rao lower bound. Track divergence statistics are analyzed. The results show that while the tracking performance of the Kalman filters is comparable to the particle filters in evaporative duct tracking, it is limited by the high non-linearity of the parabolic equation for the surface-based duct case. Particle filters, on the other hand, prove to be very promising in tracking a wide range of environments including the abruptly changing ones. Index Terms--Atmospheric ducts, extended Kalman filter (EKF), parabolic equation, particle filter (PF), refractivity-fromclutter (RFC), spatial and temporal tracking, unscented Kalman filter (UKF).

This paper describes a Markov chain Monte Carlo (MCMC) sampling approach for the estimation of not only the radio refractivity profiles from radar clutter but also the uncertainties in these estimates. This is done by treating the refractivity from clutter (RFC) problem in a Bayesian framework. It uses unbiased MCMC sampling techniques, such as Metropolis and Gibbs sampling algorithms, to gather more accurate information about the uncertainties. Application of these sampling techniques using an electromagnetic split-step fast Fourier transform parabolic equation propagation model within a Bayesian inversion framework can provide accurate posterior probability distributions of the estimated refractivity parameters. Then these distributions can be used to estimate the uncertainties in the parameters of interest. Two different MCMC samplers (Metropolis and Gibbs) are analyzed and the results compared not only with the exhaustive search results but also with the genetic algorithm results and helicopter refractivity profile measurements. Although it is slower than global optimizers, the probability densities obtained by this method are closer to the true distributions. Index Terms--Atmospheric ducts, genetic algorithms, Markov chain Monte Carlo (MCMC) techniques, radar clutter, refractivity estimation.

Ocean sound speed and its uncertainty are estimated using travel-time tomography at ranges up to 2 km using a moving source in ∼600 m water depth. The experiment included two 32-element vertical line arrays deployed about 1 km apart and a towed source at ∼10 m depth transmitting a linear frequency modulated waveform. The inversion accounts for uncertainties in the positions and velocities of the source and receivers in addition to the background sound speed state. At these short ranges, the sound speed effects are small and the representational error of the candidate forward models must be carefully evaluated and minimized. This is tested stringently by a separate position parameter inversion and by cross-validating the estimates of sound speed and arrival time, including uncertainties. In addition, simulations are used to explore the effects of adding additional constraints to the inversion and to compare the performance of moving to fixed source tomography. The results suggest that the ray diversity available from the moving source reduces the posterior sound speed uncertainty compared to the fixed source case. [ABSTRACT FROM AUTHOR]

This manuscript discusses the utility of maximal period linear binary pseudorandom sequences [also referred to as m-sequences or maximum length sequences (MLSs)] and linear frequency-modulated (LFM) sweeps for the purpose of measuring travel-time in ocean-acoustic experiments involving moving sources. Signal design and waveform response to unknown Doppler (waveform dilation or scale factor) are reviewed. For this two-parameter estimation problem, the well-known wide-band ambiguity function indicates, and moving-source observations corroborate, a significant performance benefit from using MLS over LFM waveforms of similar time duration and bandwidth. The comparison is illustrated with a typical experimental setup of a source suspended aft of the R/V Sally Ride to a depth of∼10 m and towed at∼1 m/s speed. Accounting for constant source motion, the root mean square travel-time variability over a 30 min observation interval is 53 μs (MLS) and 141 μs (LFM). For these high signal-to-noise ratio channel impulse response data, LFM arrival-time fluctuations mostly appear random while MLS results exhibit structure believed to be consistent with source (i.e., towed transducer) dynamics. We conclude with a discussion on signal coherence with integration times up to 11 MLS waveform periods corresponding to ∼27 s. [ABSTRACT FROM AUTHOR]

Merchant ship-radiated noise, recorded on a single receiver in the 360–1100 Hz frequency band over 20 min, is employed for seabed classification using an ensemble of deep learning (DL) algorithms. Five different convolutional neural network architectures and one residual neural network are trained on synthetic data generated using 34 seabed types, which span from soft-muddy to hard-sandy environments. The accuracy of all of the networks using fivefold cross-validation was above 97%. Furthermore, the impact of the sound speed and water depth mismatch on the predictions is evaluated using five simulated test cases, where the deeper and more complex architectures proved to be more robust against this variability. In addition, to assess the generalizability performance of the ensemble DL, the networks were tested on data measured on three vertical line arrays in the Seabed Characterization Experiment in 2017, where 94% of the predictions indicated that mud over sand environments inferred in previous geoacoustic inversions for the same area were the most likely sediments. This work presents evidence that the ensemble of DL algorithms has learned how the signature of the sediments is encoded in the ship-radiated noise, providing a unified classification result when tested on data collected at-sea. [ABSTRACT FROM AUTHOR]

An examination of the received spectrogram levels of about twenty merchant ship recordings on two vertical line arrays deployed on the New England continental shelf during the Seabed Characterization Experiment 2017 has identified an acoustic feature that can be attributed to the group velocities of modes 1 and 2 being equal at a frequency f = F. The observation of such a feature is a result of β n m (2 π F) = ∞ , where βnm is the waveguide invariant for modes n and m. For the New England Mudpatch, the average value of F is about 24.5 Hz. An effective seabed model is inferred from a feature inversion method that has a deep sediment layer which lies between 190 m and 290 m beneath the seafloor with sound speeds on the order of 1810 m/s. This effective sediment model appears to be consistent with a previous seismic survey on the New England shelf that identified a deep low speed layer about 250 m beneath the water sediment interface. [ABSTRACT FROM AUTHOR]

Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on different representations of the spectrograms. Multitask learning is employed; the seabed type prediction comes from classification, and the ship speed and CPA range are estimated via regression. Due to the lack of labeled field data, the CNNs are trained on synthetic data generated using measured sound speed profiles, four seabed types, and a random distribution of source parameters. Additional synthetic datasets are used to evaluate the ability of the trained CNNs to interpolate and extrapolate source parameters. The trained models are then applied to a measured data sample from the 2017 Seabed Characterization Experiment (SBCEX 2017). While the largest network provides slightly more accurate predictions on tests with synthetic data, the smallest network generalized better to the measured data sample. With regard to the input data type, complex pressure spectral values gave the most accurate and consistent results for the ship speed and CPA predictions with the smallest network, whereas using absolute values of the pressure provided more accurate results compared to the expected seabed types. [ABSTRACT FROM AUTHOR]

In this paper, a model for the bistatic reverberation associated with seafloor scattering of sound from a moving, narrowband source in an ocean waveguide is developed. Studies of the Doppler effect for moving sources in waveguides have typically focused on the forward propagating field where the Doppler shift leads to a splitting or broadening of the received spectrum. In contrast, the contributions to the scattered field come from all directions and as a consequence the spectrum of the received energy is spread across the entire range of Doppler-shifted frequencies possible for the speed of the source. The model developed here uses rays for the incident field, ray-mode analogies for the scattering, and normal modes to propagate the scattered field to the receiver. Results from this model are compared with data collected using a towed source during the Target and Reverberation Experiment 2013. The possible applications of this Doppler reverberation for seafloor characterization are also considered. [ABSTRACT FROM AUTHOR]

In coastal regions the presence of the marine boundary layer can significantly affect RF propagation. The relatively high specific humidity of the underlying 'marine layer' creates elevated trapping layers in the radio refractivity structure. While direct sensing techniques provide good data, they are limited in their temporal and spatial scope. There is a need for assessing the three-dimensional (3-D) time-varying refractivity structure. Recently published results (Gingras et al. [1]) indicate that matched-field processing methods hold promise for remotely sensing the refractive profile structure between an emitter and receive array. This paper is aimed at precisely quantifying the performance one can expect with matched-field processing methods for remote sensing of the refractivity structure using signal strength measurements from a single emitter to an array of radio receivers. The performance is determined via simulation and is evaluated as a function of: 1) the aperture of the receive array; 2) the refractivity profile model; and 3) the objective function used in the optimization. Refractivity profile estimation results are provided for a surface-based duct example, an elevated duct example, and a sequence of time-varying refractivity profiles. The refractivity profiles used were based on radiosonde measurements collected off the coast of southern California. Index Terms--Antenna arrays, electromagnetic propagation in nonhomogeneous media, refractivity estimation, signal processing, UHF radio propagation.

The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect in a shallow water environment during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used in a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs). The relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (equivalent to approximately 1 cm spatial displacement). Time-series of direct-path relative arrival-times are constructed for each VLA element across the 11 min observation interval. The overall structure of these time-series compares favorably with that predicted from an array element localization model. The short-term standard deviations calculated on the direct-path (7 μs) and bottom-reflected-path (17 μs) time-series are in agreement with the predicted arrival-time accuracies. The implications of these observed arrival-time accuracies in the context of estimating sound speed perturbations and bottom-depth are discussed. [ABSTRACT FROM AUTHOR]

For a sound field observed on a sensor array, performance of conventional high-resolution adaptive beamformers is affected dramatically in the presence of coherent multipath signals, but the directions-of-arrival (DOAs) and power levels of these arrivals can be resolved with compressed sensing (CS). When the number of multipath signals is sufficiently small, a CS approach can be used by formulating the problem as a sparse signal recovery problem. CS overcomes the difficulty of resolving coherent arrivals at an array by directly processing the sensor outputs without first estimating a sensor covariance matrix. CS is compared to the adaptive minimum-variance-distortionless-response (MVDR) spatial processor with spatial smoothing. Though spatial smoothing produces improved results by preprocessing the sensor array covariance matrix to decorrelate the coherent multipath components, it reduces the effective aperture of the array and hence reduces the resolution. An empirical study with a uniform linear array (ULA) demonstrates that CS outperforms MVDR beamformer with spatial smoothing in terms of spatial resolution and bias and variance of DOA and power estimates. Analysis of the shallow-water HF97 ocean acoustic experimental data shows that CS is able to recover the DOAs and power levels of the multipath signals with superior resolution compared to MVDR with spatial smoothing. [ABSTRACT FROM AUTHOR]

This paper considers concurrent matched-field processing of data from multiple, spatially-separated acoustic arrays with application to towed-source data received on two bottom-moored horizontal line arrays from the SWellEx-96 shallow water experiment. Matched-field processors are derived for multiple arrays and multiple-snapshot data using maximum-likelihood estimates for unknown complex-valued source strengths and unknown error variances. Starting from a coherent processor where phase and amplitude is known between all arrays, likelihood expressions are derived for various assumptions on relative source spectral information (amplitude and phase at different frequencies) between arrays and from snapshot to snapshot. Processing the two arrays with a coherent-array processor (with inter-array amplitude and phase known) or with an incoherent-array processor (no inter-array spectral information) both yield improvements in localization over processing the arrays individually. The best results with this data set were obtained with a processor that exploits relative amplitude information but not relative phase between arrays. The localization performance improvement is retained when the multiple-array processors are applied to short arrays that individually yield poor performance. [ABSTRACT FROM AUTHOR]

Matched field processing is a generalized beamforming method that matches received array data to a dictionary of replica vectors in order to locate one or more sources. Its solution set is sparse since there are considerably fewer sources than replicas. Using compressive sensing (CS) implemented using basis pursuit, the matched field problem is reformulated as an underdetermined, convex optimization problem. CS estimates the unknown source amplitudes using the replica dictionary to best explain the data, subject to a row-sparsity constraint. This constraint selects the best matching replicas within the dictionary when using multiple observations and/or frequencies. For a single source, theory and simulations show that the performance of CS and the Bartlett processor are equivalent for any number of snapshots. Contrary to most adaptive processors, CS also can accommodate coherent sources. For a single and multiple incoherent sources, simulations indicate that CS offers modest localization performance improvement over the adaptive white noise constraint processor. SWellEx-96 experiment data results show comparable performance for both processors when localizing a weaker source in the presence of a stronger source. Moreover, CS often displays less ambiguity, demonstrating it is robust to data-replica mismatch. [ABSTRACT FROM AUTHOR]

In order to carry out geoacoustic inversion in low signal-to-noise ratio (SNR) conditions, extended duration observations coupled with source and/or receiver motion may be necessary. As a result, change in the underlying model parameters due to time or space is anticipated. In this paper, an inversion method is proposed for cases when the model parameters change abruptly or slowly. A model parameter change-point detection method is developed to detect the change in the model parameters using the importance samples and corresponding weights that are already available from the recursive Bayesian inversion. If the model parameters change abruptly, a change-point will be detected and the inversion will restart with the pulse measurement after the change-point. If the model parameters change gradually, the inversion (based on constant model parameters) may proceed until the accumulated model parameter mismatch is significant and triggers the detection of a change-point. These change-point detections form the heuristics for controlling the coherent integration time in recursive Bayesian inversion. The method is demonstrated in simulation with parameters corresponding to the low SNR, 100-900 Hz linear frequency modulation pulses observed in the Shallow Water 2006 experiment [Tan, Gerstoft, Yardim, and Hodgkiss, J. Acoust. Soc. Am. 136, 1187-1198 (2014)]. [ABSTRACT FROM AUTHOR]

Surface generated ambient noise can be used to infer sediment properties. Here, a passive geoacoustic inversion method that uses noise recorded by a drifting vertical array is adopted. The array is steered using beamforming to compute the noise arriving at the array from various directions. This information is used in two different ways: Coherently (cross-correlation of upward/downward propagating noise using a minimum variance distortionless response fathometer), and incoherently (bottom loss vs frequency and angle using a conventional beamformer) to obtain the bottom properties. Compressive sensing is used to invert for the number of sediment layer interfaces and their depths using coherent passive fathometry. Then the incoherent bottom loss estimate is used to refine the sediment thickness, sound speed, density, and attenuation values. Compressive sensing fathometry enables automatic determination of the number of interfaces. It also tightens the sediment thickness priors for the incoherent bottom loss inversion which reduces the search space. The method is demonstrated on drifting array data collected during the Boundary 2003 experiment. [ABSTRACT FROM AUTHOR]

Theoretically, the empirical Green's function between a pair of receivers can be extracted from the cross correlation of the received diffuse noise. The diffuse noise condition rarely is met in the ocean and directional sources may bias the Green's function. Here matrix-based spatial filters are used for removing unwanted contributions in the cross correlations. Two methods are used for solving the matrix filter design problem. First a matrix least-square problem is solved with a low-rank approximation of the pseudo-inverse, here, derived for linear and planar arrays. Second, a convex optimization approach is used to solve the design problem reformulated with ad hoc constraints. The spatial filter is applied to real-data cross correlations of elements from a linear array to attenuate the contribution of a discrete interferer. In the case of a planar array and simulated data, a spatial filter enables a passive upgoing/downgoing wavefield separation along with an efficient rejection of horizontally propagating noise. The impact of array size and frequency band on the filtered cross correlations is discussed. [ABSTRACT FROM AUTHOR]

Measurements of ambient noise have been used to infer information about the ocean acoustic environment. In recent years the correlation of ambient noise has been shown to give estimates of the travel time of acoustic paths between the sensors recording the noise. A number of issues affect the results of the noise correlation. This paper presents the results of noise correlation of the two horizontally separated arrays of sensors in the 2010 ambient noise experiment. Using the experimental data, the effects on the convergence of the noise correlation are examined with respect to the size and shape of the arrays, the length of time used, and the directionality of the noise field. [ABSTRACT FROM AUTHOR]

Sequential Bayesian methods such as particle filters have been used to track a moving source in an unknown and space/time-evolving ocean environment. These methods treat both the source and the ocean parameters as non-stationary unknown random variables and track them via the multivariate posterior probability density function. Particle filters are numerical methods that can operate on nonlinear systems with non-Gaussian probability density functions. Particle smoothers are a natural extension to these filters. A smoother is appropriate in applications where data before and after the time of interest are readily available. Both past and 'future' measurements are exploited in smoothers, whereas filters just use past measurements. Geoacoustic and source tracking is performed here using two smoother algorithms, the forward-backward smoother and the two-filter smoother. Smoothing is demonstrated on experimental data from both the SWellEx-96 and SW06 experiments where the parameter uncertainty is reduced relative to just filtering alone. [ABSTRACT FROM AUTHOR]

A typical geoacoustic inversion procedure involves powerful source transmissions received on a large-aperture receiver array. A more practical approach is to use a single moving source and/or receiver in a low signal to noise ratio (SNR) setting. This paper uses single-receiver, broadband, frequency coherent matched-field inversion and exploits coherently repeated transmissions to improve estimation of the geoacoustic parameters. The long observation time creates a synthetic aperture due to relative source-receiver motion. This approach is illustrated by studying the transmission of multiple linear frequency modulated (LFM) pulses which results in a multi-tonal comb spectrum that is Doppler sensitive. To correlate well with the measured field across a receiver trajectory and to incorporate transmission from a source trajectory, waveguide Doppler and normal mode theory is applied. The method is demonstrated with low SNR, 100-900 Hz LFM pulse data from the Shallow Water 2006 experiment. [ABSTRACT FROM AUTHOR]

Covariance matrices of noise models are used in signal and array processing to study the effect of various noise fields and array configurations on signals and their detectability. Here, the asymptotic eigenvalues of noise covariance matrices in 2-D and 3-D attenuating media are derived. The asymptotic eigenvalues are given by a continuous function, which is the Fourier transform of the infinite sequence formed by sampling the spatial coherence function. The presence of attenuation decreases the value of the large eigenvalues and raises the value of the smaller eigenvalues (compared to the attenuation free case). The eigenvalue density of the sample covariance matrix also shows variation in shape depending on the attenuation, which potentially could be used to retrieve medium attenuation properties from observations of noise. [ABSTRACT FROM PUBLISHER]

Ambient noise was recorded on two vertical line arrays (VLAs) separated by 450 m and deployed in shallow water (depth ∼150 m) off San Diego, CA continuously for 6 days. Recordings were dominated by non-stationary and non-uniform broadband shipping noise (250 Hz to 1.5 kHz). Stable coherent noise wavefronts were extracted from ambient noise correlations between the VLAs during all 6 days by mitigating the effect of discrete shipping events and using array beamforming with data-derived steering vectors. This procedure allows the tracking of arrival-time variations of these coherent wavefronts during 6 days and may help in developing future passive acoustic tomography systems. [ABSTRACT FROM AUTHOR]

Cross-correlations of diffuse noise fields can be used to extract environmental information. The influence of directional sources (usually ships) often results in a bias of the travel time estimates obtained from the cross-correlations. Using an array of sensors, insights from random matrix theory on the behavior of the eigenvalues of the sample covariance matrix (SCM) in an isotropic noise field are used to isolate the diffuse noise component from the directional sources. A sequential hypothesis testing of the eigenvalues of the SCM reveals eigenvalues dominated by loud sources that are statistical outliers for the assumed diffuse noise model. Travel times obtained from cross-correlations using only the diffuse noise component (i.e., by discarding or attenuating the outliers) converge to the predicted travel times based on the known array sensor spacing and measured sound speed at the site and are stable temporally (i.e., unbiased estimates). Data from the Shallow Water 2006 experiment demonstrates the effectiveness of this approach and that the signal-to-noise ratio builds up as the square root of time, as predicted by theory. [ABSTRACT FROM AUTHOR]

It is well known that observations of the spatial sample covariance matrix (SCM, also called the cross-spectral matrix) reveal that the ordered noise eigenvalues of the SCM decay steadily, but common models predict equal noise eigenvalues. Random matrix theory (RMT) is used to derive and discuss properties of the eigenvalue spectrum of the data SCM for linear arrays, with an application to ocean acoustic data. Noise on the array is considered either incoherent or propagating acoustic noise that is coherent across the array. Using conventional three-dimensional or two-dimensional isotropic noise models with full or snapshot-deficient observations, realizations of the SCM eigenvalues are explained using RMT. Deep-water towed-array data are analyzed and it is shown that the eigenvalues of the SCM compare well with theory. It is demonstrated how RMT can be applied to study eigenvalue spectrum estimation as dependent on array properties (element spacing to wavelength ratio) and data sampling (snapshots). Apart from explaining the observed noise eigenvalue spectrum, the improved model of the eigenvalue spectrum has important applications in array signal processing. [ABSTRACT FROM AUTHOR]

Radar clutter in a non-standard atmosphere usually is modeled based on a single grazing angle at each range. Instead, the angular distribution of incident power can be used to obtain a more accurate model of the clutter. Angular spectral estimation provides the grazing angle distribution of propagating power. However, a large gradient in the refractivity profile, e.g., an evaporation duct, distorts plane wave propagation which in turn violates assumptions of plane wave spectral estimation. Ray tracing is used in these situations, but has its own limitations (e.g., shadow zones). We suggest using curved wave spectral estimation (CWS) that yields reliable results for any refractivity profile, in contrast to plane wave spectral estimation. CWS is used to derive multiple grazing angle clutter, a model for ocean surface clutter in the microwave region that depends on all incident angles at each range and their corresponding powers. [ABSTRACT FROM PUBLISHER]

Extracting coherent wavefronts between passive receivers using cross-correlations of ambient noise (CAN) provides a means for monitoring the seismoacoustic environment without using active sources. However, using cross-correlations between single receivers can require a long recording time in order to extract stable coherent arrivals from CAN. This becomes an issue if the propagation medium fluctuates significantly during the recording period. To address this issue, this article presents a general spatio-temporal filtering procedure to enhance the emergence rate for coherent wavefronts extracted from time-averaged ambient noise correlations between two spatially separated arrays. The robustness of this array-based CAN technique is investigated using ambient shipping noise recorded over 24 h in the frequency band [250-850 Hz] on two vertical line arrays deployed 143 m apart in shallow water (depth 20 m). Experimental results confirm that the array-based CAN technique can significantly reduce the recording duration (e.g., from 22 h to 30 min) required for extracting coherent wavefronts of sufficient amplitude (e.g., 20 dB over residual temporal fluctations) when compared to conventional CAN implementations between single pairs of hydrophones. These improvements of the CAN technique could benefit the development of noise-based ocean monitoring applications such as passive acoustic tomography. [ABSTRACT FROM AUTHOR]

The asymptotic eigenvalues are derived for the true noise covariance matrix (CM) and the noise sample covariance matrix (SCM) for a line array with equidistant sensors in an isotropic noise field. In this case, the CM in the frequency domain is a symmetric Toeplitz sinc matrix which has at most two distinct eigenvalues in the asymptotic limit of an infinite number of sensors. Interestingly, for line arrays with interelement spacing less than half a wavelength, the CM turns out to be rank deficient. The asymptotic eigenvalue density of the SCM is derived using random matrix theory (RMT) for all ratios of the interelement spacing to the wavelength. When the CM has two distinct eigenvalues, the eigenvalue density of the SCM separates into two distinct lobes as the number of snapshots is increased. These lobes are centered at the two distinct eigenvalues of the CM. The asymptotic results agree well with analytic solutions and simulations for arrays with a small number of sensors. [ABSTRACT FROM PUBLISHER]

Environmental parameters can have large spatial and temporal variability in shelfbreak regions. The capability of sequential Bayesian filters in tracking this variation is investigated. Particle filtering (PF) is used to extract the environmental parameters and their uncertainties. The method tracks the environment with fewer particles relative to conventional geoacoustic inversion methods using successive independent inversions. As an example, data from the Shallow Water 2006 Experiment are processed. The PF approach first is used to track the source and the environment with little spatial variation just northwest of the shelfbreak. Then the strongly range-dependent shelfbreak region is analyzed and the PF results are compared to previous geoacoustic inversion studies from the region. [ABSTRACT FROM AUTHOR]