Seabed classification and source localization with Gaussian processes and machine learning.

Workshop '97 data are employed for seabed classification and source range estimation. The data are acoustic fields computed at vertically separated receivers for various ranges and different environments. Gaussian processes are applied for denoising the data and predicting the field at virtual receivers, sampling the water column densely within the array aperture.

Inversion in an uncertain ocean using Gaussian processes.

Gaussian processes (GPs) can capture correlation of the acoustic field at different depths in the ocean. This feature is exploited in this work for pre-processing acoustic data before these are employed for source localization and environmental inversion using matched field inversion (MFI) in an underwater waveguide. Via the application of GPs, the data are denoised and interpolated, generating densely populated acoustic fields at virtual arrays, which are then used as data in MFI.

Introduction to the special issue on machine learning in acoustics.

The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data.

Matched field source localization with Gaussian processes.

For a sparsely observed acoustic field, Gaussian processes can predict a densely sampled field on the array. The prediction quality depends on the choice of a kernel and a set of hyperparameters. Gaussian processes are applied to source localization in the ocean in combination with matched-field processing.

Seabed classification using physics-based modeling and machine learning.

In this work, model-based methods are employed, along with machine learning techniques, to classify sediments in oceanic environments based on the geoacoustic properties of a two-layer seabed. Two different scenarios are investigated. First, a simple low-frequency case is set up, in which the acoustic field is modeled with normal modes.

Introduction to the Special Issue on Acoustic Source Localization.

Spatial localization based on acoustic observations is a rich field of interest in acoustic signal analysis. This special issue takes a close look at the diverse and growing range of problems in this area and the broad perspectives and methodologies that are presently being developed to solve them. The collection of articles presents recent advances in localization in complex and uncertain environments across a wide range of acoustic disciplines, from animal bioacoustics and acoustic signal processing in underwater environments to in air environments, architectural acoustics, and acoustic transduction.

Bayesian coherent and incoherent matched-field localization and detection in the ocean.

Matched-field processing is applied to source localization and detection of sound sources in the ocean. The source spectrum is included in the set of unknown parameters and is estimated in the localization/detection process. Bayesian broadband (multi-tonal) incoherent and coherent processors are developed, integrating the source spectrum estimation using a Gibbs sampler and are first evaluated in source localization via point estimates and probability density functions obtained from synthetic signals.

Geoacoustic inversion with generalized additive models.

Geoacoustic parameter estimation is presented as a non-linear regression problem where prediction is performed using generalized additive models applied to features extracted from broadband acoustic time-series in a machine learning framework. Qualitatively, it can be seen that signals that have propagated in different environments have distinct structures: in some cases, a single mode is identified, in others, multiple modes can be seen; signals can also be distinguished by different energy levels. Features that are employed here comprise relative amplitudes of distinct peaks in the signals, signal kurtosis, signal strength, decay of the time-series with time, and time difference between distinct peaks of the received signals.

Environmental inversion using dispersion tracking in a shallow water environment.

It has been previously shown using synthetic data that dispersion tracking with particle filtering can be used for sediment sound speed inversion. Here, dispersion tracking is performed with data collected in the Gulf of Mexico for sediment sound speed and thickness and water column depth estimation. In this experiment, sound that propagates a long distance from the source allows the identification of dispersion curves reflecting the different group velocities of modal frequencies within and across modes.

Stochastic sediment property inversion in Shallow Water 06.

Received time-series at a short distance from the source allow the identification of distinct paths; four of these are direct, surface and bottom reflections, and sediment reflection. In this work, a Gibbs sampling method is used for the estimation of the arrival times of these paths and the corresponding probability density functions. The arrival times for the first three paths are then employed along with linearization for the estimation of source range and depth, water column depth, and sound speed in the water.

Sediment sound speed inversion with time-frequency analysis and modal arrival time probability density functions.

The dispersion pattern of a received signal is critical for understanding physical properties of the propagation medium. The objective of this work is to estimate accurately sediment sound speed using modal arrival times obtained from dispersion curves extracted via time-frequency analysis of acoustic signals. A particle filter is used that estimates probability density functions of modal frequencies arriving at specific times.

Passive fathometer reflector identification with phase shift modeling.

In passive fathometer processing, the presence of wavelets in the estimate of the medium's Green's function corresponds to the location of reflectors in the seabed; amplitudes are related to seabed properties. Bayesian methods have been successful in identifying reflectors that define layer interfaces. Further work, however, revealed that phase shifts are occasionally present in the wavelets and hinder accurate layer identification for some reflectors.

Sequential filtering for dispersion tracking and sediment sound speed inversion.

Dispersion curves in ocean environments are accurately estimated from received signals through the extraction of instantaneous modal frequencies and corresponding arrival times for long-range propagation. The ultimate goal is to estimate sediment sound speed using the extracted dispersion pattern. The approach extends work previously conducted in dispersion tracking with sequential filtering, improving on the latter technique.

Sound speed estimation and source localization with linearization and particle filtering.

A method is developed for the estimation of source location and sound speed in the water column relying on linearization. The Jacobian matrix, necessary for the proposed linearization approach, includes derivatives with respect to empirical orthogonal function coefficients instead of sound speed directly. First, the inversion technique is tested on synthetic arrival times, using Gaussian distributions for the errors in the considered arrival times.

Particle filtering for arrival time tracking in space and source localization.

Locating and tracking a source in an ocean environment and estimating environmental parameters of a sound propagation medium are critical tasks in ocean acoustics. Many approaches for both are based on full field calculations which are computationally intensive and sensitive to assumptions on the structure of the environment. Alternative methods that use only select features of the acoustic field for localization and environmental parameter estimation have been proposed.

Particle filtering for passive fathometer tracking.

Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors.

Effects of Cd, Cu, Ni, and Zn on brown tide alga Aureococcus anophagefferens growth and metal accumulation.

Trace metals play important roles in regulating phytoplankton growth and could influence algal bloom development. Laboratory studies were conducted to evaluate the influence of environmentally relevant concentrations of Cd, Cu, Ni, and Zn on Aureococcus anophagefferens bloom (brown tide) development. Results show that the elevated Ni(2+) concentrations, e.

A particle filtering approach for spatial arrival time tracking in ocean acoustics.

The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers.

Two-dimensional interferometric and synthetic aperture imaging with a hybrid terahertz/millimeter wave system.

We have developed an interferometric synthetic aperture incoherent imaging system at 94 GHz, in which a high-power electronic millimeter wave source (Gunn Oscillator) is integrated with a continuous-wave terahertz (THz) photomixing detection system to achieve a high signal-to-noise ratio. Imaging of a point source located 10?m away from the detector array is presented. Two-dimensional THz reflective images of an extended object with different shapes are reconstructed with only four detectors by use of rotational synthesis.

A Bayesian approach to modal decomposition in ocean acoustics.

A Bayesian approach is developed for modal decomposition from time-frequency representations of broadband acoustic signals propagating in underwater media. The goal is to obtain accurate estimates and posterior probability distributions of modal frequencies arriving at a specific time and their corresponding amplitudes, which can be employed for geoacoustic inversion. The proposed approach, optimized via Gibbs sampling, provides uncertainty information on modal characteristics via the posterior distributions, typically unavailable from traditional methods.

Video-rate terahertz interferometric and synthetic aperture imaging.

Experimental results from a video-rate terahertz interferometric imaging system are presented. The source emits continuous narrow bandwidth radiation at 0.1 THz.

Particle filtering for dispersion curve tracking in ocean acoustics.

A particle filtering method is developed for dispersion curve extraction from spectrograms of broadband acoustic signals propagating in underwater media. The goal is to obtain accurate representation of modal dispersion which can be employed for source localization and geoacoustic inversion. Results are presented from the application of the method to synthetic data, demonstrating the potential of the approach for accurate estimation of waveguide dispersion characteristics.

Rapid-phase modulation of terahertz radiation for high-speed terahertz imaging and spectroscopy.

Rapid voltage-controlled phase modulation of cw terahertz (THz) radiation is demonstrated. By transmitting an infrared beam through a lithium niobate phase modulator the phase of the THz radiation, which is generated by the photomixing of two infrared beams, can be directly modulated through a 2pi phase shift. The 100 kHz modulation rate that is demonstrated with this technique is approximately 3 orders of magnitude faster than what can be achieved by mechanical scanning.

Terahertz interferometric and synthetic aperture imaging.

The stand-off imaging properties of a terahertz (THz) interferometric array are examined. For this application, the imaged object is in the near-field region limit of the imaging array. In this region, spherical and circular array architectures can compensate for near-field distortions and increase the field of view and depth of focus.

Gibbs sampling for time-delay-and amplitude estimation in underwater acoustics.

Multipath arrivals at a receiving sensor are frequently encountered in many signal-processing areas, including sonar, radar, and communication problems. In underwater acoustics, numerous approaches to source localization, geoacoustic inversion, and tomography rely on accurate multipath arrival extraction. A novel method for estimation of time delays and amplitudes of arrivals with maximum a posteriori (MAP) estimation is presented here.