Source Localization In Shallow Water Research Articles

A Convolutional Neural Network Combining Classification and Regression for Source Localization in Shallow Water

Machine learning (ML) has been widely applied to ocean acoustic source localization. The localization problem is often regarded as a classification problem or a regression problem in the previous works. This paper proposes a convolutional neural network (CNN) combining classification and regression (CR-CNN) for source localization in shallow water with vertical array data. The normalized sample covariance matrices (SCMs) of the broadband data received by a vertical line array calculated by an acoustic propagation model are used as the input features of the network in the training process. The proposed method is verified by the observation data in the shallow water area of the East China Sea. The results of simulation and real data show that the performance of proposed method is better than that of the separate classification CNN (C-CNN) and MFP. And the localization results of the experimental data are basically consistent with the geoacoustic parameters sensitivity analysis. The depth estimation of the CR-CNN is much better than MFP, while the range estimation performance of CR-CNN, C-CNN and MFP are equivalent.

Probabilistic focalization for shallow water localization.

Localizing and tracking an underwater acoustic source is a key task for maritime situational awareness. This paper presents a sequential Bayesian estimation method for passive acoustic source localization in shallow water. The proposed probabilistic focalization approach associates detected directions of arrival (DOAs) to modeled DOAs and jointly estimates the time-varying source location. Embedded ray tracing makes it possible to incorporate environmental parameters that characterize the acoustic waveguide. Due to its statistical model, the proposed method can provide robustness in scenarios with severe environmental uncertainty. We demonstrate performance advantages compared to matched field processing using data collected during the SWellEx-96 experiment.

Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

This paper develops a joint approach for time-evolving sound speed field (SSF) inversion and moving source localization in shallow water environment. The SSF is parameterized in terms of the first three empirical orthogonal function (EOF) coefficients. The approach treats both first three EOF coefficients and source parameters (e.g., source depth, range and speed) as state vectors of evolving with time, and a measurement vector that incorporates acoustic information via a vertical line array (VLA), and then the inversion problem is formulated in a state-space model. The processors of the extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) are used to estimate the evolution of those six parameters. Simulation results verify the proposed approach, which enable it to invert the SSF and locate the moving source simultaneously. The root-mean-square-error (RMSE) is employed to evaluate the effectiveness of this proposed approach. The interfile comparison shows that the EnKF outperform the EKF. For the EnKF, the robustness of the approach under the sparse vertical array configuration is verified. Moreover, the impact of the source-VLA deployment on the estimation is also concerned.

Cascade of blind deconvolution and array invariant for robust source-range estimation

The array invariant proposed for robust source localization in shallow water is based on the dispersion characteristics in ideal waveguides. It involves conventional plane-wave beamforming using a vertical array, exploiting multiple arrivals separated in beam angle and travel time, i.e., beam-time migration. The approach typically requires either a short pulse emitted by a source or the Green's function that can be estimated from a probe signal to resolve distinct multipath arrivals. In this letter, the array invariant method is extended to unknown source waveforms by extracting the Green's function via blind deconvolution. The cascade of blind deconvolution and array invariant for robust source-range estimation is demonstrated using a 16-element, 56-m long vertical array at various ranges (1.5–3.5 km) from a towed source broadcasting broadband communication waveforms (0.5–2 kHz) in approximately 100-m deep shallow water.

Cascade of blind deconvolution and array invariant for robust source-range estimation.

The array invariant proposed for robust source localization in shallow water is based on the dispersion characteristics in ideal waveguides. It involves conventional plane-wave beamforming using a vertical array, exploiting multiple arrivals separated in beam angle and travel time, i.e., beam-time migration. The approach typically requires either a short pulse emitted by a source or the Green's function that can be estimated from a probe signal to resolve distinct multipath arrivals. In this letter, the array invariant method is extended to unknown source waveforms by extracting the Green's function via blind deconvolution. The cascade of blind deconvolution and array invariant for robust source-range estimation is demonstrated using a 16-element, 56-m long vertical array at various ranges (1.5-3.5 km) from a towed source broadcasting broadband communication waveforms (0.5-2 kHz) in approximately 100-m deep shallow water.

Noise source localization in shallow water

The possibilities of the method for detecting a noise source, estimating its radial velocity and its distance from the receiver, based on the double Fourier transform of the interference pattern formed during motion, have been considered for a single-receiver configuration. It is shown that, as compared to a signal with a uniform spectrum, the limiting values of the input signal-to-noise ratio, at which the noise source can be localized, increase by about half. The results of a computational experiment are presented. A comparative analysis of the noise immunity of the algorithm applied to signals with uniform and noise spectra is performed.

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.

Array invariant-based source localization in shallow water using a sparse vertical array

The array invariant proposed for robust source localization in shallow water exploits the beam-time migration of broadband signals. The approach requires minimal knowledge of the environment, but involves plane wave beamforming using a short-aperture vertical array in stratified acoustic waveguides. In this paper, the array invariant approach is extended to a large-aperture vertical array that is sparse with significant variation of the sound speed across the aperture for conventional beamforming. The extension is feasible because the array invariant in shallow water utilizes surface/bottom-reflected arrivals that behave like those in ideal waveguides. Robust source-range estimation is demonstrated using data from a 16-element, 56-m long vertical array at various ranges (1.5-3.5 km) from a broadband source (0.5-2 kHz) in approximately 100-m deep shallow water.

Shallow water source localization using a mobile short horizontal array

This paper presents an approach to the challenging issue of passive source localization in shallow water using a mobile short horizontal linear array with length less than ten meters. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, the use of such small aperture passive sonar systems makes it difficult to acquire sufficient spatial gain for localizing long-range sources. To meet the requirement, a localization approach that employs matched-field based techniques that enable the short horizontal linear array is used to passively localize acoustic sources in shallow water. Furthermore, the broadband processing and inter-position processing provide robustness against ocean environmental mismatch and enhance the stability of the estimation process. The proposed approach's ability to localize acoustic sources in shallow water at different signal-to-noise ratios is examined through the synthetic test cases where the sources are located at the endfire and some other bearing of the mobile short horizontal linear array. The presented results demonstrate that the positional parameters of the estimated source build up over time as the array moves at a low speed along a straight line at a constant depth.

Robust source localization in shallow water based on vector optimization

Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.

Airborne source localization in shallow water

Airborne source localization in shallow water

Matched-field Source Localization with a Mobile Short Horizontal Linear Array in Offshore Shallow Water

Abstract Passive source localization in shallow water has always been an important and challenging problem. Implementing scientific research, surveying, and monitoring using a short, less than ten meter long, horizontal linear array has received considerable attention in the recent years. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, it is usually difficult to obtain a sufficient spatial gain for localizing long-range sources due to its limited physical size. To address this problem, a localization approach is proposed which is based on matched-field processing of the likelihood of the passive source localization in shallow water, as well as inter-position processing for the improved localization performance and the enhanced stability of the estimation process. The ability of the proposed approach is examined through the two-dimensional synthetic test cases which involves ocean environmental mismatch and position errors of the short array. The presented results illustrate the localization performance for various source locations at different signal- to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the short array moves at a low speed along a straight line at a certain depth.

Source localization in shallow water using artificial neural network

This study presents a method of passive localization for shallow water acoustic sources based on ray acoustic theory. ray acoustic theory is used to establish an underwater acoustic channel and to analyze physical quantities (arrival time and arrival angle) received by sensors, and to determine mutual relationship of rays between sources and receivers by using an artificial neural network for source localization. This ray-acoustic-based method of passive localization features simplicity, less calculation, and robust performance to environment variations. Hence, the method is more practicable and more valuable for source localization. In this study, the ray-acoustic-based method of passive localization is not only used in shallow water, but it is also verified by using a nontrained source to prove its accuracy and reliability. [Work sponsored by National Science Council.]

Environmentally robust nulling applied to rank-N stochastic matched field processing

Passive source localization in shallow water is complicated by both the uncertain propagation environment and the presence of strong interferers. Traditional spatial nulling techniques fail to accommodate environmental variability, while principle component methods neglect energy that has spread beyond a rank-1 signal characterization. One can accommodate environmental variability by treating the parameters of a propagation environment as random quantities. This yields a random acoustic pressure field at a receiver, which can be approximated by a Gaussian random vector, represented by a spatial covariance matrix. To mitigate these nulling problems, one can characterize the interfering signal in this manner. With such a model, a whitening filter can be calculated and applied to the received signal. This provides a spatial null that removes the multiple-component energy contributed by the interferer in an uncertain propagation environment. Rank-N localization techniques can then be applied, robustly estimating a target source location in the presence of environmental uncertainty. [Work supported by the DARPA Advanced Technology Office.]

Source localization in shallow water in the presence of sensor location uncertainty

The problem of source localization in shallow water in the presence of sensor location uncertainty is considered. The Cramer-Rao Bound is used to carry out a feasibility study for the joint source and sensor location problem when the multipath propagation channel is modeled as a known, deterministic waveguide. Unlike the free-space propagation channel, the boundedness of the shallow-water waveguide along its vertical axis provides the key to joint determination of the source and sensor location parameters. It is seen that, when a set of intuitive identifiability conditions are satisfied, numerical examples indicate that, for the scenarios considered, the resulting loss in accuracy with which the source location can be estimated due to sensor location uncertainty may be tolerable.

Broadband source localization in shallow water in the presence of internal waves

Source localization is a challenging problem in a time-varying random shallow-water environment in the presence of internal waves. The short correlation time of the acoustic field (a couple of minutes) makes it difficult to adjust the replica field in response to the environmental changes. Also, mode couplings induced by the internal waves are random and not predictable. In this paper, the effect of mode coupling on source localization in the presence of Garrett–Munk and solitary internal waves is investigated. It is found that mode coupling affects practically all modes in shallow water. Random-mode coupling reduces the matched-field correlation. To improve source localization, a broadband matched-beam processing algorithm is proposed to suppress the noncoherent coupled-mode contributions. It is shown that this technique significantly improves the localization performance over conventional matched-field processing.

Broadband source localization in shallow water

The null spectrum defined in multi image subspace algorithm (MISA) for source localization in shallow water possesses an interesting property, that is, when it is averaged over the signal bandwidth, its mean is found to cluster between 0.8 and 0.9, except at the actual source position where it is close to zero. Thus, the frequency-averaged null spectrum or its inverse is found to be an effective quantity for source localization. The signal peak to maximum side lobe ratio is found to increase as a square root of the number of frequency bins over which it is averaged. An approximate theoretical analysis has been carried out. The theoretical results broadly support the outcome of the numerical experiments. Further, we have shown that for broadband averaging to succeed the segment length must be much greater than the largest delay between the direct path and the furthest significant multipath.

Source localization in shallow water with internal waves

Source localization can be a difficult problem in temporally and spatially varying shallow-water environments. The SWARM 95 data indicate that the acoustic fields at 225 and 400 Hz change drastically in the time scale of several minutes. Since it is practically impossible to model the replica field at this time scale, one expects degraded performance for matched field processing (assuming a time-independent replica field). An alternate processing scheme is proposed here. First it is shown that internal waves cause mode conversions and that mode conversions degrade matched-field source localization. This is illustrated for the Garret–Munk internal waves, the solitary internal waves and the combination of both. It is then shown that source localization can be achieved by using only the uncoupled modes. This concept was implemented using matched-beam processing. The beam filter serves to suppress the contribution of the coupled modes. It is shown that this technique significantly improves the localization performance over conventional matched-field processing. [Work supported by the Office of Naval Research.]

Performance analysis of subspace based method for source localization in shallow water

The present paper aims at studying the performance characteristics of a subspace based algorithm for source localization in shallow water such as coastal water. Specifically, we study the performance of Multi Image Subspace Algorithm (MISA). Through first-order perturbation analysis and computer simulation it is shown that MISA is unbiased and statistically efficient. Further, we bring out the role of multipaths (or images) in reducing the error in the localization. It is shown that the presence of multipaths is found to improve the range and depth estimates. This may be attributed to the increased curvature of the wavefront caused by interference from many coherent multipaths.

Source localization in a partially known shallow‐water channel

The source localization in shallow water is beset with problems arising from the presence of a large number of correlated multipaths. Nevertheless, given a complete knowledge of the water channel it is definitely possible to localize a source. A complete knowledge of the channel, however, is rarely available under most practical conditions. A new approach is proposed wherein the bottom reflection coefficients are not required; hence the bottom conditions need not be known. Further, because of the use of signal subspace for localization, the proposed approach is robust against the background noise (−20 dB) and channel depth uncertainty (10λ). All these nice features of the proposed approach are possible only when the array size is large (≳40 sensors).