This paper investigates the applicability of a ray-based blind deconvolution (RBD) method for underwater acoustic sources of opportunity such as ships recorded on a receiver array. The RBD relies on first estimating the unknown phase of the random source by beamforming along a well-resolved ray path, and then matched-filtering each received signal using the knowledge of this random phase to estimate the full channel impulse responses (CIRs) between the unknown source and the array elements (up to an arbitrary time-shift) as well as recovering the radiated signal by the random source. The performance of this RBD is investigated using both numerical simulation and experimental recordings of shipping noise in the frequency band [300–800 Hz] for ranges up to several kilometers. The ray amplitudes of the estimated CIRs are shown to be consistent with known bottom properties in the area. Furthermore, CIRs obtained for an arbitrarily selected shipping track are used as data-derived replicas to perform broadband matched-field processing to locate another shipping source recorded at a later time in the vicinity of the selected track.

In this article, we propose an active target detector by using a shallow neural network (NN) with novel features under small sonar data, where deep learning (DL) models are restricted. The features are contrived by considering differences between target and clutter signals in the temporal variation of amplitude and frequency when using an active ping; these are extracted from preprocessed spectrograms of beam time series. The classification ability of the features is demonstrated through comparison with that of prevalent timbral features in the training data set that reflect the human auditory system. Finally, the detection performance of the shallow NN trained using the proposed features is compared with those from the fine-tuned DL models. During the generalization test, the DL models miss a significant number of targets in the test data set—including target signals with patterns unseen during training—and the VGG16 used for detailed comparison has a detection probability of 0.69. The probability increases to 0.86 via the shallow NN, with remarkably enhanced target detection. This demonstrates the competitive performance and generalization ability of the shallow NN, using features that consider active sonar returns under a lack of data.

This paper considers a channel parameter estimation problem using a wideband multichannel receiver array. We estimate the sparse underwater acoustic communication channel parameters, such as time-delay, incidence angle, Doppler frequency, and complex amplitude of impinging multipath components. Multichannel signals from a receiver array are modeled to obtain additional sparsity in the incidence angle and the angle-delay-Doppler-spread function is defined to parameterize the channel. To estimate the principal entries of the angle-delay-Doppler-spread function, we propose a modified version of the orthogonal matching pursuit (OMP) algorithm to utilize a redundant dictionary. We introduce a space-alternating scheme which divides the entire parameter search space into smaller subsets to avoid handling large parameter search space. The performance of the proposed method is evaluated using two experimental data: one from large-scale water tank with the capability of generating surface gravity wave and the other from shallow sea water which shows sparse channel structure. Our results demonstrate that the proposed method accurately estimates the time-varying multipath channel parameters with lower residual error than the OMP.

This paper presents a technique for solving the multichannel blind deconvolution problem. The authors observe the convolution of a single (unknown) source with K different (unknown) channel responses; from these channel outputs, the authors want to estimate both the source and the channel responses. The authors show how this classical signal processing problem can be viewed as solving a system of bilinear equations, and in turn can be recast as recovering a rank-1 matrix from a set of linear observations. Results of prior studies in the area of low-rank matrix recovery have identified effective convex relaxations for problems of this type and efficient, scalable heuristic solvers that enable these techniques to work with thousands of unknown variables. The authors show how a priori information about the channels can be used to build a linear model for the channels, which in turn makes solving these systems of equations well-posed. This study demonstrates the robustness of this methodology to measurement noises and parametrization errors of the channel impulse responses with several stylized and shallow water acoustic channel simulations. The performance of this methodology is also verified experimentally using shipping noise recorded on short bottom-mounted vertical line arrays.

The feasibility of tracking a ship radiating random and anisotropic noise is investigated using ray-based blind deconvolution (RBD) and array invariant (AI) with a vertical array in shallow water. This work is motivated by a recent report [Byun, Verlinden, and Sabra, J. Acoust. Soc. Am. 141, 797–807 (2017)] that RBD can be applied to ships of opportunity to estimate the Green's function. Subsequently, the AI developed for robust source-range estimation in shallow water can be applied to the estimated Green's function via RBD, exploiting multipath arrivals separated in beam angle and travel time. In this letter, a combination of the RBD and AI is demonstrated to localize and track a ship of opportunity (200–900 Hz) to within a 5% standard deviation of the relative range error along a track at ranges of 1.8–3.4 km, using a 16-element, 56-m long vertical array in approximately 100-m deep shallow water.

This article presents a method for improving the performance of the ray-based blind deconvolution (RBD) algorithm, which was first proposed by Sabra, Song, and Dowling [J. Acoust. Soc. Am. 127(2), EL42–EL47 (2010)]. In order to retrieve the channel impulse response (CIR), the original RBD algorithm uses the source signal phase from a selected single beam output. However, when the impinging multipath signals have low coherence, the channel estimate from a selected beam may not show all paths correctly. In this research, the maximum likelihood estimator, which is called the alternating projection, is applied to separate multipath signals. Then the multiple CIRs obtained from those separated signals are coherently combined. This results in more robust detection of existing multipaths. The performance of the proposed method is verified using Noise09 sea experiment data, where the proposed method better resolves the multipath arrival structure.