Environmental limits to source localization

In the field of underwater acoustic signal processing, broadband array signal processing is an important research issue, which is more complex than that of narrowband. Broadband passive localization of underwater acoustic sources is investigated in this study. In order to improve the ability to locate multiple radiation sources which are emitted from ships, the orthogonal propagator method (OPM) is proposed to estimate direction of arrival (DOA) for the target. Based on the theory of spatial spectrum estimation, the linear decomposition of the covariance matrix is adopted to form the orthogonal propagator, and the spatial spectrum is constructed according to orthogonality of subspaces. Then, the directions of ship radiation noises are determined by searching the extreme points of the spatial spectrum. For broadband underwater acoustic signals, the non-coherent subspace method is used to average the effective frequency points in bandwidth to reduce noise interference, and OPM algorithm operating in broadband underwater acoustic signal, on arbitrary sonar array, is derived. In order to verify this method, numerical simulations are conducted to test its performance in localization accuracy, beam width and spatial resolution. During the simulations, a uniform linear array consisting of 96 elements is designed. Additionally, comparisons between the proposed method and conventional beamforming technique are conducted. Comparison results show that the OPM method performs better in spatial directivity and spatial resolution.

For shallow underwater acoustic sources in deep-sea environments, the received signals in the direct wave zone exhibit distinct multipath arrival structures during large-depth reception, which can be exploited for passive source localization. This paper presents a method for estimating source depth and range through joint matching of arrival angles and time-delay differences between the direct wave and the first-order sea-surface reflected wave. The arrival angles are obtained from deep-sea particle velocity fields based on ray theory, while the time-delay differences are extracted from the autocorrelation function of the received signals. Simulation results show that both arrival angles and time-delay differences can be accurately estimated simultaneously at signal-to-noise ratios (SNRs) above 10 dB, allowing high-precision passive source localization with the proposed method. Under fixed source-receiver geometry, seabed topography and sea-surface roughness significantly alter acoustic propagation paths. Seabed variations affect paths of both direct and bottom-interacted waves, and sea-surface roughness specifically distorts the path of the first-order sea-surface reflected wave, thereby reducing the accuracy of time delay difference estimation in matched field processing. Validated using data from a 2020 deep-sea acoustic propagation experiment in the Western Pacific—where a single vector hydrophone captured signals from a towed source—the method effectively estimated the depth and range of shallow underwater acoustic sources within a 15 km range, with mean relative errors of 5.5% in depth and 6.2% in range. Compared to conventional methods that use only arrival angles or time-delay differences, this joint-matching strategy offers an efficient and robust solution for passive localization of underwater acoustic sources in the deep-sea direct wave zone.

Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks

Direct regressions for underwater acoustic source localization in fluctuating oceans

Multipath propagation and reverberation of underwater acoustic signal affect the accuracy of localization estimation based on time difference of arrival (TDOA). To solve the problem, a novel algorithm is proposed to improve the accuracy of localization estimation of underwater acoustic source. Based on the signals received at an array of sensors, a general framework for acoustic source localization using particle filtering is proposed. A generic particle filtering framework was derived. The simulation results demonstrate the superiority of the proposed method. The resulting particle filter is shown to outperform traditional acoustic source localization method.

Normally, detection, enumeration, and localization of sources are treated as separate problems, and different processing techniques are employed to solve them. Source enumeration or prior knowledge of the number of sources is a pre-requisite for the existing methods of source localization. In this paper, we present a method called Residual Data Vector Processing (RDVP) for joint detection and 3-dimensional (3-D) localization of an unknown number of underwater acoustic sources using measurements by a 3-D array of sensors. Theoretically, maximum likelihood estimation (MLE) is the best method of source localization since MLE provides asymptotically efficient estimates. But the computational complexity of MLE is prohibitively high. The localization accuracy of the proposed method compares very favorably with that of other methods of 3-D source localization such as Capon and MUSIC processors, while its computational complexity is much lower than that of the existing methods.

Information-theoretic measures of acoustic source localization performance provide for performance comparisons that are valid across classes of signal processing algorithms and can be used as performance criteria for the optimization of receiver-array spatial configurations for source localization. This work investigates the use of fundamental information-theoretic quantities, including mutual information of source location and processor output and conditional entropy of source location given processor output, as performance criteria for the optimization of array configurations. Applications of these criteria to the optimization of horizontal and vertical arrays are illustrated in examples of Bayesian localization, conventional beamforming, and matched-field processing of the acoustic field of a time-harmonic source in a range-independent shallow-water waveguide. The optimized array spatial configurations are compared with results obtained using traditional (energy-based) performance measures. [Work supported by ONR.]

Acoustic waves are well-suited for remote sensing applications and structural health monitoring purposes because they convey information about their source and can be measured using non-contacting methods. Source localization is an important structural health monitoring task; however, traditional time-of-flight array signal processing techniques used to localize acoustic sources are ill-suited for many structural engineering applications due to the potential for complicated propagation paths, the dispersive propagation of acoustic waves in structures, and the coupling of the vibrating structure and the surrounding medium. Thus, source localization experiments were conducted using matched field processing (MFP) for a 0.9-m diameter round aluminum plate excited by the impact of a 1.3-cm stainless-steel ball bearing dropped from 7.6-cm. A 14-sensor linear remote acoustic array placed 8.9-cm above the plate measured the sound radiated by the 0.64-cm thick plate. MFP array signal processing localization techniques were used along with a physics-based finite element acoustic model to localize the excitation on the structure. Source localization results in both a quiet environment and environment with additive white Gaussian noise are discussed, and these results are compared to those from an acoustic model where plate edge reflections are neglected. [Work was sponsored by a SMART Scholarship and the NEEC.]

Human activities in the marine environment coupled with the efficient propagation of acoustic waves, have significantly increased ocean noise levels. This rise poses a threat to marine biodiversity, especially impacting vocal-dependent marine mammals. Therefore, obtaining a more comprehensive understanding of the complex ocean soundscape, shaped by both natural and anthropogenic sources, is essential.Ocean-bottom seismometer (OBS) datasets present an opportunity to explore the marine acoustic noise field. OBS hydrophones can be used to effectively study regional marine soundscapes. Additionally, the three-component OBS seismometers capture acoustic-to-seismic conversions from the water column, enabling the detection and localization of acoustic events.Through the analysis of OBS hydrophone recordings from the NE Atlantic offshore Ireland, we develop a frequency-dependent empirical sound propagation model for the area. The model provides information on (1) the spectral amplitude decay of sound sources in the region and (2) the distance between OBSs and acoustic sources.To construct the model, we consider ship noise from individual vessels recorded by OBSs at known distances from the source, using Automatic Identification System (AIS) ship-tracking data. As a ship moves away from its closest point of approach (CPA) to an OBS, the hydrophone records the evolution of the frequency-dependent ship's acoustic signal spectral amplitude with distance. This enables us to compute amplitude decay curves for narrow frequency bands from different vessel signals recorded at various OBSs. We apply a signal-to-noise ratio (SNR) quality control criterion and consider ships sailing along 'straight' paths at a constant speed (< 2 kn variation). After normalizing and scaling the CPAs of the different curves to a reference, we stack them for each frequency band and compute polynomial fits for the stacked amplitude decay curves. We analyse the number of vessel tracks needed to build a reliable empirical model. Numerical simulations are computed to further understand the acoustic propagation properties from our empirical model.The goal is to investigate (1) how spectral amplitudes at different frequencies behave with distance from the sound sources, (2) the efficiency of frequency-dependent sound propagation in the ocean in regions with variable seafloor morphologies, and (3) the potential use of the empirical model to obtain distances to other sound sources with unknown locations, such as Baleen whales. To achieve this, we compute ratios of spectral amplitudes for pairs of different frequency bands using the fitted amplitude decay curves. We test the model's ability to locate other vessels with known AIS positions before using it to locate different sound sources with unknown positions. Locating a sound source employing the distance provided by the empirical model is valuable when OBSs are too far apart for 'traditional' multi-sensor methods. In such cases, the incoming direction of the sound source is determined by rotating the horizontal-component OBS seismic data to point to the source back-azimuth. Also, the amplitude decay information provided by the model has multiple applications in other areas where noise pollution may be a concern (e.g., marine infrastructure construction).

Acoustic emission source localization is the basic function of the application of acoustic emission technology. For complex structures, mathematical analysis positioning algorithms cannot be obtained, and accurate acoustic emission source localization cannot be obtained, which has always been one of the problems in the actual applications of acoustic emission technology. To solve this problem, this paper proposes an acoustic emission source localization method based on deep learning, which can obtain high-precision acoustic emission source positioning without the need for mathematical analysis positioning algorithms. The AI deep learning acoustic emission source localization method adopts the method of meshing, dividing the grid in the measured structure, generating label data at the grid position, using the label data to conduct AI deep learning training to establish a positioning model, and using the trained model to analyze the actual acoustic emission data (non-labeled data) to locate the acoustic emission source. This paper uses a multi-layer perceptron model to train multi-dimensional features (arrival time and amplitude). This method effectively improves the accuracy of acoustic emission source localization. Experimental results show that the positioning accuracy of the designed deep learning model in the test set reaches 99.625%, which is significantly better than the traditional Time Difference of Arrival (TDOA) positioning algorithm. In addition, this paper further verifies the stability and reliability of the model in localization tasks through credibility metrics such as Score, Margin and Entropy. This article provides a new solution for locating acoustic emission sources in complex structures, and lays a theoretical and practical foundation for the future development of non-destructive testing technology.

In practical underwater environments, the uncertain and varying propagation speed of an acoustic signal makes time-based localization of an acoustic source challenging. In this paper, we utilize the isogradient sound speed profile (SSP) model with random model parameters to address the underwater source localization problem using time-of-arrival (TOA) measurements. To better reflect the real-world environments, the SSP we used is indeterminate where its model parameters have unknown random deviations from the nominal values. We propose a two-step method for locating an underwater acoustic source for this highly nonlinear challenging problem. In the first step, we propose an approximation to the TOA model that takes the sound speed dependence on the source depth into consideration, and formulate a non-convex constrained weighted least squares (CWLS) problem to obtain an initial source position estimate. In the second step, we expand the original TOA model by the second-order Taylor series at the initial estimate, and then create a different non-convex CWLS problem to obtain a refined solution. Both CWLS problems are solved by applying the semidefinite relaxation technique. Moreover, the proposed method is extended to the case where the source is not time synchronized with the sensors. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for this particular localization problem and show by mean squared error (MSE) analysis that the proposed method is capable of achieving the CRLB performance under small Gaussian errors in the measurements and SSP model parameters. Simulation results confirm the effectiveness of the proposed method in achieving good localization performance.

A three-dimensional localization method for transient acoustic sources is developed, based on time differences between direct and surface-reflected arrivals at two hydrophones. The method accounts for refraction caused by a depth-dependent sound-speed profile using a ray-theoretic approach, and, further, it provides localization error estimates accounting for uncertainties of the arrival times and hydrophone locations, as well as for depth-dependent uncertainties in the sound-speed profile. In the first of two steps, source depth and range to each hydrophone are estimated using an iterative, linearized Gauss-Markov inversion scheme. In the second step, the estimated source ranges are combined with the hydrophone locations to obtain the source location in the horizontal. Localization performance is analyzed in a simulation study, and the linearized localization estimates and uncertainties are validated by comparison with a fully-nonlinear (but numerically intensive) Markov-chain Monte Carlo inversion. [Wor...

In this paper, an acoustic source localization method based on 4-element cross-array is presented, and an acoustic source localization system is designed. Four acoustic sensors are employed to collect the acoustic source signal, after several steps as amplifying, processing, AD conversion, generalized cross-correlation function method is used to compute the time difference of the signal arrival to the four sensors, then the position of the acoustic source can be determined. Experiments show that our system can achieve good performance in both speed and accuracy. The acoustic source localization system can be incorporated into a motion estimation system with low cost inertial sensors to increase estimation accuracy.

Acoustic source localization in an anisotropic plate without knowing its material properties – A new approach

In acoustical imaging and source localization, several methods for the exploitation of asynchronous array measurements have been proposed, based on covariance matrix completion, Bayesian estimation, fusion of beamforming maps, etc. The goal of asynchronous measurements is to reach the performance of large and dense arrays, with reduced experimental effort, by moving an array of limited size between experiments, and fuse the obtained data to produce an estimation of the distribution of acoustical sources. In this study, we consider the performances that one can expect from asynchronous measurements, and investigate the actual performances of several methods from the state of the art. In particular, the mean squared errors of the estimation of the position and power of an acoustical source are estimated using simulations and experimental measurements.