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The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

This work describes a method for extracting the time-domain Green’s function between two ships in a shallow-water environment. First, the individual Green’s functions from each ship are estimated along the vertical receiver array using blind deconvolution. Then, the Green’s function between two ships is obtained simply by either correlation or convolution of the individual Green’s functions, depending on the array position with respect to the ships. Simulation and experimental results are presented and discussed to demonstrate the feasibility of extracting the Green’s functions between ships radiating random broadband noise (100–500 Hz), using a 56.25 m long vertical array in about 100-m deep shallow water.

The array invariant, a robust approach to source-range estimation in shallow water, is based on the dispersion characteristics of broadband signals in ideal waveguides. It involves time-domain planewave beamforming using a vertical line array (VLA) to separate multiple coherent arrivals (eigenrays) in beam angle and travel time. Typically, a probe signal (i.e., a cooperating source) is required to estimate the Green’s function, but the array invariant has been recently extended to a ship of opportunity radiating random signals using a ray-based blind deconvolution. Still, one major drawback is its sensitivity to the array tilt, shifting the beam angles and adversely affecting the array invariant parameter that determines the source range. In this paper, a simple optimization algorithm for simultaneous estimation of the array tilt and the source range is presented. The method is applied to a ship of opportunity (200–900 Hz) circling around a 56-m long VLA at a speed of 3 knots (1.5 m/s) at ranges of 1.8-3.6 km in approximately 100-m deep shallow water. It is found that the standard deviation of the relative range error significantly reduces to about 4%, from 14% with no compensation of the array tilt.

Machine learning (ML) is the idea that there are generic algorithms that can tell us something interesting about a set of data without us having to write any custom code specific to the problem. Instead of writing code, we feed data to the generic algorithm and it builds its own logic based on the data. In this paper, ML is applied to classification of multiple (4) sources at various depths in a shallow-water waveguide where the input data are time-varying channel impulse responses (CIRs) received by a vertical array (4 hydrophones) from each of the sources over a period of 22 hours (528 examples). Specifically, the first 16 hours of CIRs are used for training a 4-layered CNN (convolutional neural net) to exploit the structure of CIRs in the two-dimensional time and depth domain, similar to image processing. Our hypothesis is that the dataset collected over a semi-diurnal tidal period (>12 hours) could capture the internal representations or feature vector needed for classification. The trained model is then tested on the next 6 hours of CIRs and achieves 98% accuracy, indicating the potential of ML in underwater applications.

Time reversal (TR) is the process of generating a spatio-temporal focus at a probe source (PS) location by backpropagating a time-reversed version of a received signal. While TR focusing requires the PS for a coherent acoustic focus at its origin, the requirement of the PS has been partially relaxed by the concept of a virtual source array (VSA) [J. Acoust. Soc. Am. 125, 3828–3834 (2009)]. A VSA can serve as a remote platform or lens and redirect a focused field to a selected location beyond the VSA for which the field is assumed as a homogeneous medium with constant sound speed. The objective of this study is to extend VSA-based single TR focusing to simultaneous multiple focusing. This is achieved using the optimization theory by employing the multiple constraints method derived from a constraint matrix, which consists of appropriately synchronized transfer functions. It is found that simultaneous multiple focusing can be achieved with distortionless response at selected multiple locations, and its performance degrades in the presence of sound speed mismatch. Possible applications for the VSA-based TR focusing are discussed, and numerical simulation results are presentedTime reversal (TR) is the process of generating a spatio-temporal focus at a probe source (PS) location by backpropagating a time-reversed version of a received signal. While TR focusing requires the PS for a coherent acoustic focus at its origin, the requirement of the PS has been partially relaxed by the concept of a virtual source array (VSA) [J. Acoust. Soc. Am. 125, 3828–3834 (2009)]. A VSA can serve as a remote platform or lens and redirect a focused field to a selected location beyond the VSA for which the field is assumed as a homogeneous medium with constant sound speed. The objective of this study is to extend VSA-based single TR focusing to simultaneous multiple focusing. This is achieved using the optimization theory by employing the multiple constraints method derived from a constraint matrix, which consists of appropriately synchronized transfer functions. It is found that simultaneous multiple focusing can be achieved with distortionless response at selected multiple locations, and its perf...

Time reversal (TR) exploits spatial diversity to achieve spatial and temporal focusing in complex environments. Over the last decade its concept has been applied successfully to underwater acoustic communications in time-varying multipath environments with robustness and computational simplicity, as an alternative to conventional adaptive multichannel equalization. Temporal focusing (pulse compression) mitigates the intersymbol interference (ISI) and subsequent channel equalization removes the residual ISI, thus providing nearly optimal performance in theory. The spatial focusing capability facilitates multi-user communications without an explicit use of time, frequency, or code division, while an adaptive time reversal approach can further reduce the crosstalk among users. TR communications can be easily extended to time-varying channels using a block-based approach with periodic channel updates. This talk will present an overview of TR communications and recent advances including bidirectional equalization, single- versus multi-carrier approach, and communications with autonomous vehicles such as an AUV or Glider.