Bayesian source localization with uncertain Green's function in an uncertain shallow water ocean

Source depth estimation with feature matching using convolutional neural networks in shallow water

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes.

Multifrequency matched-field source localization based on Wasserstein metric for probability measures

Matched-field processing (MFP) for underwater source localization serves as a generalized beamforming approach that assesses the correlation between the received array data and a dictionary of replica vectors. In this study, the processing scheme of MFP is reformulated by computing a statistical metric between two Gaussian probability measures with the cross-spectral density matrices (CSDMs). To achieve this, the Wasserstein metric, a widely used notion of metric in the space of probability measures, is employed for developing the processor to attach the intrinsic properties of CSDMs, expressing the underlying optimal value of the statistic. The Wasserstein processor uses the embedded metric structure to suppress ambiguities, resulting in the ability to distinguish between multiple sources. In this foundation, a multifrequency processor that combines the information at different frequencies is derived, providing improved localization statistics with deficient snapshots. The effectiveness and robustness of the Wasserstein processor are demonstrated using acoustic simulation and the event S5 of the SWellEx-96 experiment data, exhibiting correct localization statistics and a notable reduction in ambiguity. Additionally, this paper presents an approach to derive the averaged Bartlett processor by evaluating the Wasserstein metric between two Dirac measures, providing an innovative perspective for MFP.

Passive ranging of a moving target in the direct-arrival zone in deep sea using a single vector hydrophone

The vector hydrophone covers more comprehensive sound field information over the sound pressure hydrophone. The passive target ranging method based on the shallow-located unmanned platform equipped with vector hydrophones is explored based on the measured surface ship data acquired in the deep-sea area in December 2021. First, a bearings-only target motion analysis (TMA) algorithm is proposed, determining the motion state of a moving target (e.g., azimuth angle, abeam time, heading angle, and the ratio of velocity to initial distance) in accordance with its horizontal bearing time recording (BTR) data. Subsequently, the loss function is built following the situation information and the average complex acoustic intensity to determine the real-time grazing angle of the target. Last, the real-time range of the target is determined in accordance with the situation information and the grazing angle. As revealed by the comparison of the calculated value of the sea trial data with the GPS trajectory, the average distance error of the target reaches 11.6%, the root mean square error (RMSE) of the grazing angle is 1.99°, the RMSE of the velocity is 1.9 kn, and the RMSE of the abeam time reaches 51.4 s.

A deep neural network approach to acoustic source localization in a shallow water tank experiment

In this paper, an acoustic source localization method using the emerging technology of the deep neural network (DNN) is proposed. After the construction and training of the DNN, the capability of the DNN for source localization through a set of numerical simulations is verified. Next, experimental studies and demonstrations in a very shallow water tank with acoustic reflective walls are prepared, which enable the quick acquisition of a huge amount of experimental data for the training of a one-dimensional DNN-based source localization model. The development of the DNN-based source localization method and the corresponding numerical and experimental demonstration constitute the main contribution of this work. The associated performance is then evaluated at various frequencies. In particular, the localization results of the DNN are compared with readily available model-based localization methods, such as the conventional matched field processing method and the normal-mode based multiple signal classification method. The comparison shows that the proposed DNN approach is able to produce satisfactory accuracy in this reflective shallow water tank environment, for which a forward acoustic propagating model is not required. Last but not least, the generality of the proposed DNN approach from one-dimensional localization to progressively more complicated two-dimensional tasks is also considered.

Stochastic matched-field localization of an acoustic source based on principles of Riemannian geometry

Passive localization of acoustic sources is treated within a geometric framework where non-Euclidean distance measures are computed between a cross-spectral density estimate of received data on a vertical array and a set of stochastic replica steering matrices, rather than traditional replica steering vectors. A processing scheme involving matrix-matrix comparisons where steering matrices, as functions of the replica source coordinates, naturally incorporate environmental variability or uncertainty provides a general framework for considering the acoustic inverse source problem in an ocean waveguide. Within this context a subset of matched-field processors is examined, based on recent advances in the application of non-Euclidean geometry to statistical classification of data feature clusters. The matrices are interpreted abstractly as points in a Riemannian manifold, and an appropriately defined distance measure between pairs of matrices on this manifold defines a matched-field processor for estimating source location. Acoustic simulations are performed for a waveguide comprising both a depth-dependent sound-speed profile perturbed by linear internal gravity waves and a depth-correlated surface noise field, providing an example of the viability of this approach to passive source localization in the presence of sound-speed variability.

Source localization using deep neural networks in a shallow water environment

Deep neural networks (DNNs) are advantageous for representing complex nonlinear relationships. This paper applies DNNs to source localization in a shallow water environment. Two methods are proposed to estimate the range and depth of a broadband source through different neural network architectures. The first adopts the classical two-stage scheme, in which feature extraction and DNN analysis are independent steps. The eigenvectors associated with the modal signal space are extracted as the input feature. Then, the time delay neural network is exploited to model the long term feature representation and constructs the regression model. The second concerns a convolutional neural network-feed-forward neural network (CNN-FNN) architecture, which trains the network directly by taking the raw multi-channel waveforms as input. The CNNs are expected to perform spatial filtering for multi-channel signals, in an operation analogous to time domain filters. The outputs of CNNs are summed as the input to FNN. Several experiments are conducted on the simulated and experimental data to evaluate the performance of the proposed methods. The results demonstrate that DNNs are effective for source localization in complex and varied water environments, especially when there is little precise environmental information.

Sub-antenna processing for coherence loss in underwater direction of arrival estimation

This paper deals with the loss of coherence in underwater direction-of-arrival estimation. The coherence loss, which typically arises from dynamical ocean fluctuations and unknown environmental parameters, may take the form of a multiplicative colored random noise applied to the measured acoustic signal. This specific multiplicative noise needs to be addressed with methodological developments. This paper proposes a weighting process that locally mitigates the effects of the coherence loss. More specially, a set of coherent sub-antennas is designed from the so-called Mutual Coherence Function (MCF). The assessed source position results from the combination of each sub-antenna by using a mixed norm. Two experiments are considered in the paper: either a random noise is sampled to simulate the effect of random ocean fluctuations, or a synthetic acoustic waveguide is used in which the coherence loss is due to some multipath interferences. It is shown that the weighting process allows for a decrease in the estimation error in comparison to a Conventional Beamformer (CB).

Multiple-array passive acoustic source localization in shallow water

This paper considers concurrent matched-field processing of data from multiple, spatially-separated acoustic arrays with application to towed-source data received on two bottom-moored horizontal line arrays from the SWellEx-96 shallow water experiment. Matched-field processors are derived for multiple arrays and multiple-snapshot data using maximum-likelihood estimates for unknown complex-valued source strengths and unknown error variances. Starting from a coherent processor where phase and amplitude is known between all arrays, likelihood expressions are derived for various assumptions on relative source spectral information (amplitude and phase at different frequencies) between arrays and from snapshot to snapshot. Processing the two arrays with a coherent-array processor (with inter-array amplitude and phase known) or with an incoherent-array processor (no inter-array spectral information) both yield improvements in localization over processing the arrays individually. The best results with this data set were obtained with a processor that exploits relative amplitude information but not relative phase between arrays. The localization performance improvement is retained when the multiple-array processors are applied to short arrays that individually yield poor performance.