Paul A. Baxley

Shallow-water sparsity-cognizant source-location mapping

Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.

Channel characterization for high‐frequency acoustic communications

Undersea internets or seawebs are currently being developed and tested for a variety of applications in which some kind of device (e.g., an ADCP, hydrophone, vertical line array, autonomous undersea vehicle) needs to pass information to another (e.g., radio buoy, surface ship). The underlying physical layer is often an acoustic modem. However, the ocean medium, though it can carry acoustic waves with remarkable fidelity to long distances, is also somewhat unreliable. Storms may cause network outages by both driving up the ambient noise and corrupting the clarity of the acoustic mirror formed by the air–sea interface. Meanwhile, as the SONAR community is well aware, the refractive effects of the ocean can lead to sweet spots and dead zones (shadows). The required sound output of a modem varies accordingly. We have been conducting a program (SignalEx) to: (1) better understand propagation in the high‐frequency range (around 10 kHz), currently of greatest interest for modems; (2) understand how the environme...

Three‐dimensional Gaussian beam tracing for shallow‐water applications

Gaussian beam tracing is a ray‐based method of acoustic wave propagation that overcomes some of the implementation problems of conventional ray methods, while retaining a speed advantage over wave theory approaches. A fan of Gaussian beams are propagated from a source, according to the standard ray equations, so that the pressure at any given point can be approximated by the sum of contributions from each beam. Eigenray computations, perfect shadows, and infinite caustics associated with standard ray methods are thereby eliminated, and the applicability to lower‐frequency problems is improved. Gaussian beam tracing is particularly well suited for three‐dimensional problems, for which eigenray computations are costly. The effectiveness of a three‐dimensional Gaussian beam model [Bucker, J. Acoust. Soc. Am. 95, 2437–2440 (1994)] for predicting acoustic propagation in shallow water is demonstrated. At low frequencies, Gaussian beam and wave solutions are compared for the ASA benchmark wedge problem and the S...

Quantitative Performance Comparison Among Processors in MFP

Data on a tilted line a array (TLA) from the 1996 Shallow Water Cell Experiment (SWellEx-96), which was performed in 200 meters of water over a relatively flat bottom, are used to quantitatively evaluate the performance of processors used in matched field processing (MFP). The MVDR processor, the dominant-mode rejection processor and the partially-adaptive reduced-rank processor have been evaluated using data on a tilted line array (TLA). According to this evaluation, the MVDR processor with white noise gian constraint (WNGC) has the best performance, followed by the dominant-mode rejection processor, the partially-adaptive reduced-rank processor and the linear processor.

Compressive sensing methods for passive localization and tracking of undersea acoustic sources

Matched-field processing techniques can achieve localization of undersea acoustic sources in both range and depth when sufficient environmental information is available. Unfortunately, these techniques are sensitive to environmental mismatch and often fail when localizing multiple acoustic sources. This work presents a family of acoustic source-localization techniques that similarly to matched-field processing exploit environmental information for localizing acoustic sources in both range and depth. Unique features of these methods are their explicit use of a sparse representation of the source-localization map and ability to model environmental mismatch. Tools from the areas of compressive sensing and mathematical optimization are leveraged for developing computationally tractable solvers that enable fast processing of high-dimensional source-localization maps. These localization techniques are also extended for tracking multiple acoustic sources. In this case, it is possible to exploit the inherent spar...

A Multitask Learning Framework for Broadband Source-Location Mapping Using Passive Sonar

Underwater source localization via passive sonar is a challenging task due to the dynamic and complex nature of the acoustic environment. Different from approaches based on matched-field processing, this work explores broadband underwater source localization within a multitask learning (MTL) framework. Here, each task refers to a robust signal approximation problem over a single frequency. MTL provides a natural framework for exchanging information across the narrowband signal-approximation problems and constructing an aggregate (across frequencies) source-localization map. Efficient algorithms based on block coordinate descent (BCD) are developed for solving the source-localization problem. Complex-valued predictor screening rules for reducing the computational complexity of the algorithm are also developed. These rules discard map locations from the set of possible source locations prior to using BCD. They reduce the computational complexity of the localization algorithm without compromising the localization results. Tests of these approaches on synthetic and real data for the SWellEX-3 environment compare the performance of the proposed algorithm to that of alternative methods.

Broadband acoustic-source localization using passive sonar via multitask learning

Passive sonar is an attractive technology for underwater acoustic-source localization that enables the localization system to conceal its presence and does not perturb the maritime environment. Notwithstanding its appeal, passive-sonar-based localization is challenging due to the complexities of underwater acoustic propagation. Different from alternatives based on matched-field processing whose localization performance severely deteriorate when localizing multiple sources and when faced with model mismatch, this work casts the broadband underwater acoustic-source localization problem as a multitask learning (MTL) problem, thereby enabling robust and high-resolution localization. Here, each task refers to a sparse signal approximation problem over a single frequency. MTL provides an elegant framework for exchanging information across the individual regression problems and constructing an aggregate (across frequencies) source localization map. The localization problem is formulated as a stochastic least-squ...

Sparsity-Cognizant Source Location Mapping for Underwater Acoustics

Matched-field processing (MFP) is a generalization of classical beamforming that has been traditionally used in underwater source localization problems. However, MFP suffers from low resolution, sensitivity to model mismatch, and is challenged when more than one source is present. This work develops a robust high-resolution underwater source localization algorithm that capitalizes on the sparsity inherent in the underwater source localization problem. Similar to MFP, the sparsity-cognizant approach developed here capitalizes on a model for the acoustic propagation environment and casts the localization problem as a regularized least-squares (LS) one. The resulting regularizer encourages sparsity on the grid-based source location map. An efficient solver whose computational complexity scales linearly with the grid size is developed and its performance illustrated via numerical tests.

High-Frequency Propagation for Acoustic Communications

In recent years there has been great progress in developing undersea wireless networks. The physical layer of these networks is generally an acoustic link operating in the 10–50 kHz band. Interestingly, our understanding of the acoustic propagation has not kept up with the elegant signaling schemes used to transmit the information. For instance, one common system uses adaptive equalizers to not only recombine the ocean multipath but to track the ocean dynamics. The tracking occurs on a millisecond time scale. Predicting system performance then requires an understanding of the transmission loss, the noise background, and the dynamics of the multipath. Here we use the term ‘transmission loss’ loosely, glossing over subtleties about the coherent and the incoherent field, which also play an important role in the system performance. This talk will summarize the issues and our knowledge about them, drawing upon results from an extensive set of sea tests conducted under the SignalEx program.

Broadband matched‐field tracking with horizontal line arrays in shallow water

The use of three‐dimensional matched‐field processing to obtain time‐dependent range, depth, and bearing estimates with a horizontal line array (HLA) can be computationally restrictive. The problem becomes much more tractable, however, when beam forming is first used to obtain bearing estimates versus time, which are then used to obtain range‐depth ambiguity surfaces versus time. For the case of a moving source, matched‐field tracking (MFT) compensates for source motion by integrating matched‐field correlations over candidate source tracks through the bearing‐range‐depth space. The true track is determined by the highest integrated correlation, which also results in a gain in detectibility for the true source track. The effectiveness of this approach for the tracking of broadband sources in shallow water is studied via the analysis of HLA data collected during the Shallow Water Evaluation Cell Experiment 1996 (SWellEX‐96), which occurred in 200 m water, 6 km southwest of San Diego. Broadband signals in th...