The method presented in this paper assumes that the received signal is a linear combination of delayed and attenuated uncorrelated replicas of the source emitted waveform. The set of delays and attenuations, together with the channel environmental conditions, provide sufficient information for determining the source location. If the transmission channel is assumed known, the source location can be estimated by matching the data with the acoustic field predicted by the model conditioned on the estimated delay set. This paper presents alternative techniques that do not directly attempt to estimate time delays from the data but, instead, estimate the subspace spanned by the delayed source signal paths. Source Localization is then done using a family of measures of the distance between that subspace and the subspace spanned by the replicas provided by the model. Results obtained on the INTIMATE'96 data set, in a shallow-water acoustic channel off the coast of Portugal, show that a sound source emitting a 300-800-Hz LFM sweep could effectively be localized in range or depth over an entire day.

This paper proposes a vector sensor measurement model and the related Bartlett estimator based on particle velocity measurements for generic parameter estimation, illustrating the advantages of the Vector Sensor Array (VSA). A reliable estimate of the seabed properties such as sediment compressional speed, density and compressional attenuation based on matched-field inversion (MFI) techniques can be achieved using a small aperture VSA. It is shown that VSAs improve the resolution of seabed parameter estimation when compared with pressure sensor arrays with the same number of sensors. The data considered herein was acquired by a four-element VSA in the 8–14 kHz band, during the Makai Experiment in 2005. The results obtained with the MFI technique are compared with those obtained with a method proposed by C. Harrison, which determines the bottom reflection loss as the ratio between the upward and downward beam responses. The results show a good agreement and are in line with the historical information for the area. The particle velocity information provided by the VSA increases significantly the resolution of seabed parameter estimation and in some cases reliable results are obtained using only the vertical component of the particle velocity.

This paper aims at estimating the azimuth, range and depth of a cooperative broadband acoustic source with a single vector sensor in a multipath underwater environment, where the received signal is assumed to be a linear combination of echoes of the source emitted waveform. A vector sensor is a device that measures the scalar acoustic pressure field and the vectorial acoustic particle velocity field at a single location in space. The amplitudes of the echoes in the vector sensor components allow one to determine their azimuth and elevation. Assuming that the environmental conditions of the channel are known, source range and depth are obtained from the estimates of elevation and relative time delays of the different echoes using a ray-based backpropagation algorithm. The proposed method is tested using simulated data and is further applied to experimental data from the Makai’05 experiment, where 8–14 kHz chirp signals were acquired by a vector sensor array. It is shown that for short ranges, the position of the source is estimated in agreement with the geometry of the experiment. The method is low computational demanding, thus well-suited to be used in mobile and light platforms, where space and power requirements are limited.

This paper proposes a novel design for the localization system of autonomous underwater vehicles (AUVs) using acoustic signals. The solution presented exploits models based on kernel functions with two main purposes: 1) to reject outliers; and 2) to correct or improve accuracy of measurements. The localization system discussed is based on well-established techniques such as support vector data description (SVDD) and autoassociative kernel regression (AAKR) derived from machine learning theory that utilizes heuristic models for classification and regression tasks, respectively. By coupling the algorithm to the navigation system, we seek to reduce the sensitivity of the localization scheme to the reflected acoustic waves or fluctuations of underwater channel properties without modifying the solution used for data fusion or overloading the algorithm embedded in the vehicle. Data collected in the field with a light underwater vehicle (LAUV) were used to demonstrate the advantages of the proposed approach.

Acoustic predictions of the recently developed traceo ray model, which accounts for bottom shear properties, are benchmarked against tank experimental data from the EPEE-1 and EPEE-2 (Elastic Parabolic Equation Experiment) experiments. Both experiments are representative of signal propagation in a Pekeris-like shallow-water waveguide over a non-flat isotropic elastic bottom, where significant interaction of the signal with the bottom can be expected. The benchmarks show, in particular, that the ray model can be as accurate as a parabolic approximation model benchmarked in similar conditions. The results of benchmarking are important, on one side, as a preliminary experimental validation of the model and, on the other side, demonstrates the reliability of the ray approach for seismo-acoustic applications.

Travel-time-based tomography is a classical method for inverting sound-speed perturbations in an arbitrary environment. A linearization procedure enables relating travel-time perturbations to sound-speed perturbations through a kernel matrix. Thus travel-time-based tomography essentially relies on the inversion of the kernel matrix and is commonly called “linear inversion.” In practice, its spatial resolution is limited by the number of resolved and independent arrivals, which is a basic linear algebra requirement for linear inversion performance. Physically, arrival independency is much more difficult to determine since it is closely related to the sound propagating channel characteristics. This paper presents a brief review of linear inversion and shows that, in deep water, the number of resolved arrivals is equal to the number of independent arrivals, while in shallow water the number of independent arrivals can be much smaller than the number of resolved arrivals. This implies that in shallow water there are physical limitations to the number of independent travel times. Furthermore, those limitations are explained through the analysis of an equivalent environment with a constant sound speed. The results of this paper are of central importance for the understanding of travel-time-based shallow water tomography.