Passive source localization with intersecting spherical surfaces from time delay estimates

This paper addresses the problem of direct-path passive broadband localization of a shallow-water acoustic source with a sparsely distributed set of sensors in the presence of uncertain multipath propagation. The classical approach to broadband source localization with sensors spaced many wavelengths apart involves time delay estimation (TDE) via generalized cross-correlation (GCC) methods. In shallow-water scenarios, however, multipath arrivals yield spurious peaks in cross-correlation outputs resulting in anomalous estimates of source location. In this paper, cross-correlations are normalized by an expected direct-path delay and geometrically averaged over multiple array orientations in order to disambiguate multipath returns from direct-path arrivals. In shallow-water channels, this allows for more robust estimates of the intersensor time delay due to direct-path propagation. Simulation results indicate a significant improvement in source localization performance over GCC methods operating in multipath environments.

For the burst-type acoustic emission signal, time delay estimation of two sensors calculated by signal cross-correlation is not accurate, and it leads the source localization results is not also accurate. A new method is proposed to improve accuracy of source localization results based on multi-scale analysis and multi-sensors. Acoustic emission signal multi-scale analysis using wavelet transform, then the most accurate time delay is selected in sub-band of signal multi-scale analysis by multi-sensors time delay vector close rule. Finally, the simulation acoustic emission source localization experiment has high accuracy.

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.

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.

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.

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.]

In recent years some progresses have been made in acoustic source localization (ASL) in highly anisotropic plates when the plate material properties are not known. Some simplifying assumptions in many of the earlier source localization techniques that the wave propagates along a straight line from the source to the receiving sensor is not correct for many anisotropic plates. Wave energy in anisotropic solids generally propagate along curved lines and forms non-circular wave fronts. As a result, for highly anisotropic solids the acoustic source localization techniques that assume straight-line propagation of waves from the source to the receiver are bound to give erroneous results. In this paper earlier techniques as well as new developments for acoustic source localization in homogeneous isotropic and anisotropic plates are discussed. The newest source localization techniques in anisotropic plates analyze the non-circular shapes of the wave front. Following this analysis, the acoustic source has been successfully localized in some anisotropic plates without knowing the plate properties. Limitations of the current techniques that still exist and require further investigation are discussed.

Time-delay estimation (TDE) sensors can be used to effectively locate a transmitting source. Our aim is to use distributed TDE sensors that are relatively inexpensive. The latter is the result of two constraints: a single analog to digital converter is used for sampling the sum of two sensor output signals, and the sampling interval is substantially larger than the practically required time delay resolution. This approach leads to the requirement for sub-sample interval delay estimation from overlapping different echoes of a received signal. No specific a priori assumptions are made regarding the nature of the received signal. We adapt the time-frequency ratio of mixtures (TFRM) approach for blind source separation for our TDE sensor. Our experimental set-up consists of two microphones placed at non-equal distances from an acoustic source. The TDEs are then used for source localization.

Acoustic source localization in heterogeneous media

An Odyssey IIb autonomous underwater vehicle (AUV) made by Bluefin Robotics, Inc., was acquired by the Marine Physical Laboratory, Scripps Institution of Oceanography, to conduct research in underwater acoustics as well as provide a platform for other scientific studies. The original Odyssey IIb tail cone was replaced with a ducted fan, vectored thrust system installed on vehicles currently sold by Bluefin. In initial sea tests with the new thrust system, the acoustic self noise levels of the vehicle while underway were 20 to 50 dB higher than typical ocean background noise levels, preventing the vehicle's use as a receiver of low level sounds. Controlled tests were performed to characterize the radiated and vibration noise of the AUV propulsion and actuators. Once this baseline was established, changes were made, mostly to the tail cone propulsion, to decrease the vehicle's self noise. The resulting self noise levels of the AUV from 10 Hz up to 10 kHz measured while underway by a hydrophone mounted on the AUV's inner shroud now are at or below typical shallow water background noise levels except in three bands; below 250 Hz, around 500 Hz, and from 0.9 to 2.0 kHz. The goal of this paper is to describe these changes and their effects in lowering vehicle noise levels.

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).

An intelligent algorithm was developed based on backpropagation artificial neural network for the acoustic emission source localization. The established and trained methods of the algorithm were stated with the time difference of arrival detected by a fiber optic acoustic emission sensor array and the coordinates of acoustic emission source. The response characteristic of fiber optic acoustic emission sensor was calibrated with the commercial piezoelectric ceramic transducer (PZT) acoustic emission sensor, which provided that the fiber optic acoustic emission sensor was better suited to detect the low frequency of stress wave than the PZT sensor. Four fiber optic acoustic emission sensors were deployed in a square array in an aluminum plate for comparisons between different algorithms of source localization. Comparison results of acoustic emission source location provided that the intelligent algorithm improved the accuracy by reducing the nonlinear errors. For the anisotropic materials, a sensor array deployed in a diamond pattern was adopted. The velocities of stress waves in orthogonal directions were measured as the basic performance for both algorithms of source localization. Four sensors were integrated into a carbon fiber–reinforced polymer plate as a perfect structure for locating the acoustic emission source impacting on its surface. The experiment results provide that the maximum error is only 6.3 mm using the intelligent algorithm.

The material will generate elastic waves during the damage, and analyze the damage signal to achieve accurate positioning of the material damage, which is called acoustic emission (AE) damage source localization. The carbon fiber composite material plate is an anisotropic material, and the propagation speeds of the acoustic emission waves are different in different directions, and it is difficult to achieve accurate positioning of the damage by conventional methods. In this paper, a method based on wavelet neural network for acoustic emission damage source location is proposed. Firstly, the characteristics of the mode wave and the wave velocity propagation characteristics in the carbon fiber composite board are analyzed. The appropriate acoustic emission signal mode is selected as the measurement signal. Then, according to the propagation velocity of the modal wave along different directions of the carbon fiber composite plate, the relationship between the velocity and the angle of the carbon fiber is fitted; the time difference between the arbitrary position of the carbon fiber composite plate and the four sensors is obtained by the fitting speed relationship formula, and the time difference is calculated. The relationship between the time difference of all positions and the positioning position; then some data is selected as the training data, and the wavelet is passed. The damage localization model of carbon fiber composite board was established by neural network. Finally, the acoustic emission source localization experiment of carbon fiber composite board was carried out to prove the correctness of this method.

Accurate localization in harsh indoor environments has long been a challenging problem due to the presence of multipath. Since joint direction-of-arrival (DOA) and time delay (TD) estimation has the capability to separate the line-of-sight signal from multipath signals in the TD space, it has recently become a key technique for accurate indoor localization in next-generation WiFi and 5G networks. This paper addresses the problem of joint azimuth, elevation, and TD estimation of multiple reflections of a known signal. First, we propose an efficient approximate maximum likelihood algorithm for this problem, which updates the DOA and TD parameters alternatingly. This algorithm applies to arbitrarily distributed (planar or 3-D) arrays. Then, we present the closed-form Cramer–Rao bound for joint DOA and TD estimation, based on which we provide further analysis to show the benefit of joint DOA and TD estimation over DOA-only estimation. Although the benefit of joint estimation has been empirically shown long ago, our analysis is the first theoretical proof of it. Finally, simulation results have been provided to demonstrate the theoretical finding and the effectiveness of the new algorithm. Matlab code for the new algorithm is available at https://github.com/FWen/JADE.git .

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