Acoustic Source Localization – Recent Advances and Remaining Challenges

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

Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses crosscorrelation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.

Acoustic source localization in heterogeneous media

A two-step hybrid technique for accurately localizing acoustic source in anisotropic structures without knowing their material properties

Source localization of EEG signals identifies the loci of neural activity that generates the EEG voltage or power distribution on the scalp. This chapter provides a practical guide to source localization and its usage in EEG analysis in the frequency domain. Techniques proposed to overcome the inverse problem of source localization have progressed from equivalent dipole modeling to distributed source modeling, such as sLORETA and eLORETA. This chapter discusses the rationale behind these techniques, as well as the advantages and disadvantages of using different techniques for source localization. Recent advances in high-resolution structural MRI make it possible to accurately delineate and model the tissues inside the head. This chapter discusses the importance of using realistic head models constructed with anatomical MRIs for EEG source localization. Finally, it reviews the applications of source localization as a neuroimaging tool in EEG frequency analysis.

Composite materials are increasingly being used in the structure of aircraft. However, this type of materials is commonly encountering damage companied with acoustic emission (AE) events. In order to ensure the safety of structures during service, it is urgent to develop structural health monitoring methods able to localize AE source in composite components as they occur. Kundu et al [1] for the first time proposed a technique using L-shaped clusters to localize AE source in anisotropic plates without knowing the pre-defined velocity. However, the direction dependent group velocity may lead to a large error if the group velocities in some anisotropic materials vary widely. In this paper, whether the characteristic of anisotropy has a great influence on the localization results and the principle of how this characteristic affects the AE source localization results are investigated. The results show that localization results of isotropic plate are obviously better than that of anisotropic plate due to the direction dependent velocities of anisotropic plate, which proves that the characteristic of anisotropy will affect AE source localization results. It is also found that the variation of localization orientation error is consistent with the local velocity gradient.

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.]

This paper tackles the issue of acoustic source localization using an array of microphones. From the conventional method to advanced beamforming, the algorithms are presented from a theoretical point of view. The advantages and drawbacks of each method are then discussed. Finally, simulation results are presented.

This paper deals with recent advances in acoustic experimental methods and especially acoustic imaging. The paper covers two areas of interest to acousticians. In the first part, it is explained how near-field acoustic holography (NAH) can be extended with beamforming in the near-field, focalization. The combination of the two methods is providing now a source localization solution with a good spatial resolution over the complete frequency range without the burden of measuring a large number of points as would be required if only NAH was used. In the second part of the paper, a method is described that goes one step beyond source localization. It is interesting to locate the noise sources, but from an engineering standpoint it is even more interesting to know the internal sources/forces causing these noise sources. In this part is explained how source localization techniques in conjunction with artificial excitation of the structure can provide information on the internal sources of the structure.

Whenever acoustic source localization is considered, the assumed central hypothesis is the linearity of the acoustic wave. This hypothesis allows direct and inverse problems to be solved faster using the linearized Euler Equations or ray-tracing, for example. However, when considering high amplitude waves - acoustic shockwaves generated from a sniper shot, for instance - such simplification induces errors in the localization process. In the literature, acoustic source localization results relying on the linear hypothesis are considered to be acceptable without knowing how much error is made. This paper focuses on quantifying the errors due to non-linear effects on the source localization when the linear hypothesis is assumed. The proposed methodology consists of quantifying the accuracy of the sound source position by solving the Euler equations with a spectral difference scheme of order 3 for different configurations. First, a reference case in the linear regime is carefully computed. Then, the wave topologies are modified to resemble typical acoustical shockwaves: cylindrical N-wave signals from direct propagation and wall interaction are considered. The assessment is performed on multiple wave strengths and reflection angles to get a wide range of configurations from which errors due to the non-linear reflection will be thoroughly analysed.

The source localization problem concerns the detection and localization of an emitter whose transmission is observed by geographically separated receivers. The passive multistatic radar (PMR) problem concerns the detection and localization of a target that scatters an illumination signal to geographically separated receivers. By modeling the scattering target as an emitter, the techniques of source localization can be applied to the PMR problem. Indeed, this approach has recently been introduced in the literature. However, the exact relationship between the two problems has not been made explicit. In this work, we derive a centralized generalized likelihood ratio test (GLRT) detector that performs the processing characteristic of both source localization and PMR. This detector is used to assess the relative detection benefit provided by source localization in PMR. We show that source localization techniques are of limited utility in PMR due to the SNR regimes of the target-scattered and direct-path signals typical of the PMR signal environment.

Advances in Bayesian computational technology in the last decade have enabled the development of new source separation and source localization algorithms. These algorithms are greatly improved by the encoding of prior information about a specific problem in the form of the chosen relevant model parameters, the assignment of the likelihood functions, and the assignment of the prior probabilities of the model parameter values. I refer to such source separation algorithms as informed source separation for the reason that they are endowed with specific and often vital information about the problem. Furthermore, the Bayesian methodology allows source separation to be united with source localization simply by including the model parameters that are of interest to the researcher. Here, I will discuss the union of source separation and source localization under the Bayesian methodology, the incorporation of prior information, and the construction of an informed algorithm using the new computational technologies that allow us to estimate the values of the parameters that define these high‐dimensional problems.

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.

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.

Equivalent dipole source localization is a well-established approach to localizing the electrical activity in electroencephalogram (EEG). So far, source localization has been used primarily in localizing the epileptic source in human epileptic patients. Currently, source localization techniques have been applied to account for localizing epileptic source among the epileptic patients. Here, we present the first application of source localization in the field of sleep spindle in mouse brain. The spatial distribution of cortical potential was obtained by high density EEG and then the anterior and posterior sleep spindles were classified based on the K-mean clustering algorithm. To solve the forward problem, a realistic geometry brain model was produced based on boundary element method (BEM) using mouse MRI. Then, we applied four different source estimation algorithms (minimum norm, eLORETA, sLORETA, and LORETA) to estimate the spatial location of equivalent dipole source of sleep spindles. The estimated sources of anterior and posterior spindles were plotted in a cine-mode that revealed different topographic patterns of spindle propagation. The characterization of sleep spindles may be better be distinguished by our novel visualization method.