Passive Broadband Acoustic Source Localization Based on Orthogonal Propagator Method

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.

Extension of the operational range of underwater location techniques has been related to the incorporation of reliable acoustic propagation models, that closely predict the behaviour of oceans. In this paper, the performance of ocean tomography and passive localization of underwater acoustic sources is studied, by analyzing the coupling of performance degradations imposed by modeling mismatches on the ability to estimate the position of acoustic sources.

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.

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.

Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks

We present a multi-microphone multi-speaker direction of arrival (DOA) tracking algorithm. In the proposed algorithm, the DOA values are discretized to a set of candidate DOAs. Accordingly, and following the W-disjoint orthogonality (WDO) property of the speech signal, each time-frequency (TF) bin in the short-time Fourier transform (STFT) domain is associated with a single DOA candidate. The conditional probability of each TF observation given its corresponding DOA association, is modeled as a multivariate complex-Gaussian distribution, with the power spectral density (PSD) of each source an unknown parameter. By applying the Fisher-Neyman factorization, it can be shown that this conditional probability is proportional to the signal-to-noise ratio (SNR) at the outputs of minimum variance distortionless response (MVDR)-beamformers (BFs), directed towards all candidate DOAs. We model these observations as either a frequency-wise parallel Hidden Markov Model (HMM) or as a coupled HMM with coupling between adjacent frequency bins. The posterior probability of these associations is inferred by applying an extended FB (FB) algorithm, and the actual DOAs can be inferred from this posterior. An experimental study demonstrates the benefits of the proposed algorithm using both a simulated dataset and real recordings drawn from the acoustic source localization and tracking (LOCATA) dataset.

This paper presents a method for locating arbitrarily time-dependent acoustic sources in a free field in real time by using only four microphones. This method is capable of handling a wide variety of acoustic signals, including broadband, narrowband, impulsive, and continuous sound over the entire audible frequency range, produced by multiple sources in three dimensional (3D) space. Locations of acoustic sources are indicated by the Cartesian coordinates. The underlying principle of this method is a hybrid approach that consists of modeling of acoustic radiation from a point source in a free field, triangulation, and de-noising to enhance the signal to noise ratio (SNR). Numerical simulations are conducted to study the impacts of SNR, microphone spacing, source distance and frequency on spatial resolution and accuracy of source localizations. Based on these results, a simple device that consists of four microphones mounted on three mutually orthogonal axes at an optimal distance, a four-channel signal conditioner, and a camera is fabricated. Experiments are conducted in different environments to assess its effectiveness in locating sources that produce arbitrarily time-dependent acoustic signals, regardless whether a sound source is stationary or moves in space, even toward behind measurement microphones. Practical limitations on this method are discussed.

Direct regressions for underwater acoustic source localization in fluctuating oceans

The problem of passive localization of an acoustic source is a classic area of research in underwater acoustics. While the three-dimensional problem presents difficulties, most past investigations have been able to make suitable approximations to reduce the localization to one in a plane involving two dimensions. However, acoustic source localization in anechoic tanks, pressure vessels, or other similar enclosures often requires a truly three-dimensional formulation. This paper presents the equations describing source localization in three dimensions, given time delays between four receiving channels. Numerical difficulties associated with their solution are discussed, particularly those involved in the nonlinear solution of sets of intersecting hyperboloids. An alternate formulation is presented which makes use of intersecting spheres rather than hyperboloids. This reduces the numerical complexity markedly since finding intersections of closed surfaces such as spheres is inherently more stable than for open surfaces like hyperboloids. Several possible algorithms for implementing this approach are presented, and the practical limitations of each for use with nonideal data are contrasted. Results indicate that the proposed method of intersecting spheres is numerically simpler and provides a more accurate solution for passive source localization than the method of intersecting hyperboloids. [Work partially supported by NAVSEA PMS 409 and by NAVSEA 63X5.]

In tomoelastography, to achieve a final wave speed map by combining reconstructions obtained from all spatial directions and excitation frequencies, the use of weights is inevitable. Here, a new weighting scheme, which maximizes the signal-to-noise ratio (SNR) of the final wave speed map, has been proposed. To maximize the SNR of the final wave speed map, the use of squares of estimated SNR values of reconstructed individual maps has been proposed. Therefore, derivations of the SNR of the reconstructed wave speed maps have become necessary. Considering the noise on the complex MRI signal, the SNR of the reconstructed wave speed map was formulated by an analytical approach assuming a high SNR, and the results were verified using Monte Carlo simulations (MCSs). It has been assumed that the noise remains approximately Gaussian when the image SNR is high enough, despite the nonlinear operations in tomoelastography inversion. Hence, the SNR threshold was determined by comparing the SNR computed by MCSs and analytical approximations. The weighting scheme was evaluated for accuracy, spatial resolution and SNR performances on simulated phantoms. MR elastography (MRE) experiments on two different phantoms were conducted. Wave speed maps were generated for simulated 3D human abdomen MRE data and experimental human abdomen MRE data. The simulation results demonstrated that the SNR-weighted inversion improved the SNR performance of the wave speed map by a factor of two compared to the performance of the original (i.e., amplitude-weighted) reconstruction. In the case of a low SNR, no bias occurred in the wave speed map when SNR weighting was used, whereas 10% bias occurred when the original weighting (i.e., amplitude weighting) was used. Thus, while not altering the accuracy or spatial resolution of the wave speed map with the proposed weighting method, the SNR of the wave speed map has been significantly improved.

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.

In this paper, localization and separation of acoustic sources are examined. Depending on the number of sources in relation to the array channels, the problem is investigated in terms of underdetermined and overdetermined configurations. In the underdetermined configuration, virtual monopole sources are assumed in uniformly spaced angles. The problem is then formulated into compressive sampling (CS) problem which can be solved by using the linearly constrained -norm convex (CVX) optimization. The solution yields the directions of real sources and the source signal spectrum, which enables localization and reconstruction of sources at one shot. In the underdetermined configuration, source localization and signal separation is carried out in two steps. First, the directions of arrival (DOA) are estimated with Minimum Variance Distortionless Response (MVDR) or Multiple Signal Classification (MUSIC). Next, Tikhonov regularization (TIKR) is utilized to recover the source spectrum. In the localization problem for both configurations, Neyman-Pearson detector is employed to determine thresholds for source detection. Numerical and experimental results show that the proposed methods produce improved speech quality in terms of mean opinion score (MOS) in perceptual evaluation of speech quality (PESQ) test.

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.]

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.

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