Source Localization Accuracy Research Articles

Data-Driven Nonlinear TDOA for Accurate Source Localization in Complex Signal Dynamics

Data-Driven Nonlinear TDOA for Accurate Source Localization in Complex Signal Dynamics

Indoor Sound Source Localization via Inverse Element-Free Simulation Based on Joint Sparse Recovery

Indoor sound source localization is a key technique in many engineering applications, and an inverse element-free method based on joint sparse recovery in a Bayesian framework is proposed for reverberant environments. In this method, a discrete wave model is constructed to represent the relationships between the sampled sound pressure and the source intensity distribution, and localization in the reverberant environment is realized via inversion from the wave model. By constructing a compact supporting domain, the source intensity can be sparsely represented in subdomains, and the sparse Bayesian framework is used to recover the source intensity. In particular, joint sparse recovery in the frequency domain is exploited to improve the recovery performance. Numerical and experimental verifications show that, compared with another state-of-the-art method, the proposed method achieves high source-localization accuracy and low sidelobes with low computational complexity in highly reverberant environments.

Deep Learning-Based Dereverberation for Sound Source Localization with Beamforming

In this work, an algorithm that combines deep-learning based dereverberation and beamforming is proposed for sound source localization in the reverberant environment. The contribution is combining deep-learning based dereverberation and beamforming together. Through deep learning, the proposed algorithm can directly achieve dereverberation of the cross-spectral matrix of microphone array measurement signals in the reverberant environment, which can overcome the challenge of requiring multiple measurements like average beamforming. In this way, the proposed algorithm can be applied to the reverberant environment that requires efficient sound source localization. At the same time, the network of deep-learning based dereverberation is trained directly using the cross-spectral matrix of signals collected by microphone arrays, which can overcome the challenge of requiring prior knowledge of reflective surfaces like empirical dereverberation beamforming. In this way, the proposed algorithm can be applied to the reverberant environment composed of walls with unknown sound reflection coefficients. Both specular reflections, diffuse reflections, and background noise are considered in the reverberant environment. The cross-spectral matrix of microphone array measurement signals in the reverberant environment is first fed into the U-net for dereverberation and then combined with beamforming for sound source localization. The results in the test set show that the proposed algorithm can eliminate the influence of reverberation on sound source localization. The applicability limitation results of the proposed algorithm show the proposed algorithm’s strong robustness to unseen sound reflection coefficient, unseen SNR, and expandability to some degree for unseen source location. However, the proposed algorithm cannot provide stable and accurate sound source localization results under unseen room geometry. The proposed algorithm has higher spatial resolution and smaller errors than the average beamforming and the empirical dereverberation beamforming.

Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter

The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method.

TOA-based direct localization in shallow water multipath environments: CRLB analysis and optimal sensor deployment

Existing research explores the optimal sensing deployment in line-of-sight conditions, leveraging Cramér-Rao Lower Bound (CRLB) for optimization analysis. However, determining an optimal sensing deployment for direct localization in shallow water, characterized by distinctive multipath effects, poses a more significant challenge. This paper studies the optimal sensor deployment for direct localization grounded on the Time of Arrival (TOA) in a shallow water multipath environment. To address this problem, an initial derivation of the CRLB for accurate source localization is conducted, bearing in mind a multipath signal loss model. Subsequently, an optimization problem is articulated to minimize the trace of CRLB while adhering to the angular and range constraints to ascertain optimal sensor placement. Two necessary conditions for the objective function to attain a theoretical minimum are drawn out to find the closed-form solution. Nevertheless, it is impractical to satisfy these conditions concurrently under multipath circumstances. To swiftly attain the (sub-)optimal solution, an improved Particle Swarm Optimization (IPSO) algorithm is proposed, employing the previously derived conditions to initialize and update the inertia weights. Extensive simulation results based on the BELLHOP toolbox corroborate the theoretical analysis and the superior performance of the suggested sensor deployment scheme.

Source localization in signed networks with effective distance

While progress has been made in information source localization, it has overlooked the prevalent friend and adversarial relationships in social networks. This paper addresses this gap by focusing on source localization in signed network models. Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance, we propose an optimization method for observer selection. Additionally, by using the reverse propagation algorithm we present a method for information source localization in signed networks. Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization, and the higher the ratio of propagation rates between positive and negative edges, the more accurate the source localization becomes. Interestingly, this aligns with our observation that, in reality, the number of friends tends to be greater than the number of adversaries, and the likelihood of information propagation among friends is often higher than among adversaries. In addition, the source located at the periphery of the network is not easy to identify. Furthermore, our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization, compared with three strategies for observer selection based on the classical full-order neighbor coverage.

How does Layer Normalization improve Batch Normalization in self-supervised sound source localization?

Self-supervised sound source localization is usually challenged by the unexpected large input and incorrect direction of normalization in current solutions. A promising way for this challenge is to avoid feature deformation by incorporating more effective normalization, which is the motivation of this study. Based on the mathematical derivation of Layer Normalization (LN) in scale independence, in this work, a correspondence consolidation method is proposed to reinforce the audio–visual correspondence. By ensembling input feature normalization and LN-based simsiam Predictor, a joint gradient stabilization can be further achieved for more accurate sound source localization. Substantial experiments conducted on SoundNet-Flickr and VGG-Sound Source datasets have verified a superior performance in comparison to the other state-of-the-art works.

A novel position estimation method for wayside pass-by noise sources based on Doppler effect correction

The localization of moving noise sources has garnered substantial attention in recent years, prompting researchers to develop a range of methodologies for positioning sound sources across diverse scenarios. Despite this progress, the intricate interplay of complex spatial sound fields and the Doppler effect during motion presents a formidable challenge in achieving accurate sound source localization. To address these challenges, this paper proposes a novel method for estimating positions of wayside pass-by noise sources based on Doppler Effect Correction (Do-bPE). The proposed method capitalizes on a spatial microphone array to capture signals emanating from moving sound sources. Each channel’s sound signal is transformed into the time–frequency domain using Short Time Fourier Transform (STFT). The Approximate Greatest Common Divisor (AGCD) algorithm is then applied to extract the instantaneous frequency of the moving source from the time–frequency spectrogram. By leveraging nonlinear least squares fitting and the acoustic Doppler model, this instantaneous frequency facilitates the kinematic parameters estimation of Doppler effect, which provides the distances between each microphone within the array and the trajectory of moving sound source. Then, by employing the weighted least squares method to solve the relationship between the sound source and the array microphones, an accurate spatial position for the sound source can be obtained. The method successfully applies the Doppler effect to estimate the source distances, subsequently refining position estimation accuracy through microphone array processing. Simulation and experimental results show that the method’s positioning accuracy and robustness are significantly improved compared to existing approaches.

Abstract 17188: Electrocardiographic Algorithms for Localization Ventricular Ectopy From the Ventricular Outflow Tracts Demonstrate Poor Accuracy and Agreement

Background: Premature ventricular contractions (PVCs) and ventricular tachycardia (VT) commonly arise from the outflow tracts of the left and right ventricles and can be treated with catheter ablation when symptomatic or associated with cardiomyopathy. Accurate localization of these ectopic sources aids procedural planning. Several different electrocardiographic (ECG) algorithms have been published, but their relative performance has not been assessed. Objective: Compare the accuracy and agreement of three ECG algorithms for PVC localization. Methods: ECGs from patients who underwent catheter ablation for PVCs or VT originating the outflow tract region were analyzed using three published PVC localization algorithms. The predictions from these algorithms were then compared to the site of successful catheter ablation (right ventricular outflow tract [RVOT], left ventricular outflow tract [LVOT], or epicardium). The accuracy and agreement of the three algorithms were compared. Results: A total of 43 patients were evaluated and ectopy was suppressed via ablation from a single chamber in 37 (86%) of patients. VT/PVCs originated from the RVOT (N=10), LVOT (N=26), and epicardium (N=1). The three studied algorithms demonstrated accuracy rates of 46-70%. The algorithms disagreed on the predicted ectopy location for 19 (51%) patients. When all algorithms agreed (N=18), the prediction was accurate for only 11 patients (61%). Conclusion: Rules-based ECG algorithms demonstrate poor performance and use of different published algorithms frequently result in disparate results. More accurate and consistent methods incorporating individualized electroanatomic variation may improve accuracy and periprocedural management for PVC and VT ablation.

Identification of broadband acoustical sources in the cylindric duct using a hybrid reconstruction method

Beamforming and inverse methods are widely applied to the localization and reconstruction of acoustical sources inside ducts. Identification of in-duct sources using the conventional beamforming method would lead to poor spatial resolution and even fail at some specific frequencies with multiple virtual sources. Employing the inverse method would improve the accuracy of inner duct source localization, however, severe deviation of source intensity results calculated by the inverse method from actual results would occur due to tremendous dimensions of inversion and regulation process of the transfer function matrix. To reduce the dimensions of inversion and further improve the accuracy of source identification, a combined imaging methodology is proposed. The beamforming method is firstly applied to locate regions containing sound sources preliminarily, then an inverse method is applied to regions contributed by beamforming to remove ghosts and side lobes. Numerical simulations are implemented to validate the method. Azimuthal and radial localization results indicate that the combined imaging method is with better spatial resolution than beamforming or inverse methods. Reconstructed results of the source's volumetric velocity demonstrate that the proposed method has a lower deviation of source intensity than the inverse method.

Automatic co-registration of OPM-MEG and MRI using a 3D laser scanner

Small and lightweight optically pumped magnetometers (OPMs) have led to the development of wearable magnetoencephalography (MEG). Wearable and flexible configuration features are the highlights of OPMs; however, they present challenges for MEG and magnetic resonance imaging (MRI) co-registration. The co-registration of MEG and MRI is a precondition for source localization and significantly affects the spatial accuracy of source localization. In this study, we proposed an automatic algorithm for co-registration, which consists of two parts: helmet-to-head registration and head-to-MRI registration. For helmet-to-head registration, a fast point feature histogram (FPFH) descriptor was used to determine the sensor locations and orientations on the scanned data. For head-to-MRI registration, the nose region was precisely cropped and aligned. The results showed that the algorithm has excellent performance in speed (average time of 15 s) and accuracy (rotation error of 0.15∘ and translation error of 0.29 mm), which is the highest accuracy in relevant research.

Real-time microseismic evaluation of coalbed methane reservoir stimulation based on improved metaheuristic inversion strategy

For the enhancement of coalbed methane production, the microseismic source location plays a crucial role in evaluating fracture information during hydraulic fracturing (HF). A swift and precise microseismic source location is essential for optimizing HF schemes. This optimization necessitates an inversion strategy characterized by low computational costs and high optimization accuracy. In this study, we propose an SM-MFO inversion strategy that functions in parallel with the simplex (SM) algorithm and is founded on the original moth flame optimizer (MFO) algorithm. This strategy was validated through gas blast microseismic monitoring field tests to assess the source location performance of the SM-MFO inversion strategy, followed by its application in real-time evaluation of staged HF for the Xinji 1# horizontal well. The results reveal that within the SM-MFO inversion strategy, SM can prevent local convergence by leveraging the capabilities of MFO. Conversely, MFO can bolster local search abilities with the assistance of SM. As a result, both the convergence speed and global optimization abilities of the strategy are enhanced. In field tests, the average location error of the SM-MFO algorithm was 18.11 m. This figure is more favorable compared to the errors in the MFO algorithm (22.15 m) and the SM algorithm (35.83 m). Moreover, the SM-MFO inversion strategy facilitates real-time evaluation of HF with a computational cost of just 1.01 s for a single microseismic event. Upon analysis of the overall incidence across the nine stages in the radial direction, a span of 270 m is attained, with fewer frequency of fractures observed in the southeast direction at each respective stage Additionally, the geometric parameters of the fractures and the stimulated reservoir volume (SRV) exhibit a positive correlation with the total fluid volume. The injected fluid volume peaks in the second and sixth stages, leading to fracture mesh dimensions of 312 m and 316 m, respectively. In summary, the total SRV of the Xinji 1# horizontal well is 201.7 × 105 m³, and the reservoir stimulation effect aligns with the anticipated results. This work highlights the efficacy of the SM-MFO inversion strategy in enhancing the efficiency and accuracy of microseismic source location for hydraulic fracturing, contributing valuable insights to the field.

An application of dereverberation and multiplicative beamforming techniques for imaging noise sources generated by boat engines

This paper demonstrates the application of a dereverberation algorithm for localizing noise sources emitted from two-stroke boat engines in three dimensions domain. The engines considered have a maximum RPM of 1500 and 1200 cc. It is important to note that these engines are operated with both petrol and CNG. The investigation primarily focuses on the noise generated under various loading conditions and aims to differentiate the noise characteristics between petrol and CNG engine operations. The dereverberation algorithm is applied to enhance the accuracy of noise source localization in this study, it involves filtering an approximate optimal window width to retain data related to the direct field while removing reflections through multiplication with an appropriate Hanning window. The analysis showed that the dereverberation algorithm effectively improves the imaging source map compared to conventional beamforming maps in the 3-D domain. To compute the beamforming output, data has been recorded from five different sides of the engine, and the multiplicative beamforming technique is employed. The findings highlight the significance of dereverberation and multiplicative beamforming techniques in accurately localizing noise sources in boat engine applications.

Shift-variant deconvolution beamforming using Implicit Neural Representation for near-field acoustic image measurement

The conventional beamforming (CBF) output can be expressed as a convolution of the source distribution and the array dependent point spread function (PSF), which is defined as the response of the beamformer to a point source. The resolution and accuracy of sound source localization can be improved by deconvolution of CBF. The shift-invariant PSF assumption is only a good approximation when the source region is small compared to the distance between the array and the source. However, the PSF of the near-field underwater acoustic map measurement is shift-variant, whose mismatch with the beam could lead to performance degradation. In this paper, we proposed an optimized deconvolution method by using the neural network called implicit neural representation (INR) to solve the beam mismatch problem, due to its strong performance in learning and reconstructing spatial scenes. Rather than recompute the PSF for each pixel in the scene, INR predicts the complex-valued weight matrix in two-dimensional space that encapsulates both the spatially varying properties of the PSF and the scatter distribution of the scene. Numerical simulations and experimental results verify the effectiveness of our method. And this work can be broadly applied to arrays with shift-variant PSFs.

Design and characterisation of a 3-D microphone array for windtunnel measurements

A three-dimensional (3-D) array of GRAS 40PH-10 CCP Free-field Array Microphones has been designed for use in the UNSW anechoic wind tunnel (UAT) for aeroacoustic measurements. The array consists of 192 microphones split into three identical planar arrays of 64 microphones which are placed on both sides and above the potential core of the open-jet that issues into the wind tunnel test section. The planar arrays feature an Underbrink spiral comprised of eight arms of eight microphones each. The spiral configuration was selected to maximize signal-to-noise ratio (SNR) and minimize the beamwidth given the geometrical restrictions of the UAT. A white noise speaker was placed at a range of locations within the testing domain to characterize the source localization abilities of the array as a function of position and frequency. Furthermore, a pair of independent white noise speakers were placed at various separation distances to determine the array’s capability to identify multiple sources within the domain. A 3-D implementation of conventional cross-spectral beamforming and Clean-SC deconvolution algorithms was used, and the performance of each was compared. It was determined that Clean-SC was the preferred method for 3-D beamforming in terms of source localization accuracy, SNR, and beamwidth, despite the higher computational cost.

Graph neural networks in EEG spike detection

Objective:This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. Methods:Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. Results:On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. Conclusion:Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. Significance:This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.

The influence of volcano topographic changes on infrasound amplitude: lava fountains at Mt. Etna in 2021

Infrasound signals are used to investigate and monitor active volcanoes during eruptive and degassing activity. Infrasound amplitude information has been used to estimate eruptive parameters such as plume height, magma discharge rate, and lava fountain height. Active volcanoes are characterized by pronounced topography and, during eruptive activity, the topography can change rapidly, affecting the observed infrasound amplitudes. While the interaction of infrasonic signals with topography has been widely investigated over the past decade, there has been limited work on the impact of changing topography on the infrasonic amplitudes. In this work, the infrasonic signals accompanying 57 lava fountain paroxysms at Mt. Etna (Italy) during 2021 were analyzed. In particular, the temporal and spatial variations of the infrasound amplitudes were investigated. During 2021, significant changes in the topography around the most active crater (the South East Crater) took place and were reconstructed in detail using high resolution imagery from unoccupied aerial system surveys. Through analysis of the observed infrasound signals and numerical simulations of the acoustic wavefield, we demonstrate that the observed spatial and temporal variation in the infrasound signal amplitudes can largely be explained by the combined effects of changes in the location of the acoustic source and changes in the near-vent topography, together with source acoustic amplitude variations. This work demonstrates the importance of accurate source locations and high-resolution topographic information, particularly in the near-vent region where the topography is most likely to change rapidly and illustrates that changing topography should be considered when interpreting local infrasound observations over long time scales.

Optical Co-Registration Method of Triaxial OPM-MEG and MRI.

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X- and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Orthogonal matching pursuit algorithm based on weighted cosine similarity for sound source localization

Abstract Compressive sensing overcomes the limitations of the Nyquist criteria and is one of the most widely used compressive sensing reconstruction algorithms. Orthogonal matching pursuit (OMP) algorithm is simple, in terms of hardware implementation, and has high computational efficiency. However, the OMP algorithm exhibits poor identification performance for low-frequency sound sources and results in large localization deviations when the mesh spacing of the focus plane is small. In this study, a novel atom selection criterion based on weighted cosine similarity was proposed to improve the OMP algorithm for sound source localization and characterization. This method replaces the original inner product criterion to measure the correlation between the column vectors of the sensing matrix and the residuals, which addresses the atom selection error caused by the high correlation between atoms. Numerical simulations and experimental results show that the proposed method has a stronger anti-noise interference capability and higher accuracy for sound source identification with fewer sampling points, particularly in low-frequency and low signal-to-noise ratio environments. Compared to other OMP algorithms, the proposed method improves the performance of the OMP algorithm in sound source localization and widens the sound frequency range. This study is valuable for achieving highly accurate sound source localization and reducing measurement costs in practical applications.

Realistic three-layer head phantom for optically pumped magnetometer-based magnetoencephalography

The advent of optically pumped magnetometer-based magnetoencephalography (OPM-MEG) has introduced new tools for neuroscience and clinical research. As it is still under development, the achievable performance of OPM-MEG remains to be tested, particularly in terms of source localization accuracy, which can be influenced by various factors, including software and hardware aspects. A feasible approach to comprehensively test the performance of the OPM-MEG system is to utilize a phantom that simulates the actual electrophysiological properties of the head while ensuring the precise locations of dipole sources. However, conventional water or dry phantoms can only simulate a single-sphere head model. In this work, a more realistic three-layer phantom was designed and fabricated. The proposed phantom included the scalp, skull, and cortex tissues of the head, as well as the simulated dipole sources. The scalp and cortex tissues were simulated using an electrolyte solution, while the dipole source was constructed from a coaxial cable. All main structures in the phantom were produced using 3D printing techniques, making the phantom easy to manufacture. The fabricated phantom was tested on a 36-channel OPM-MEG system, and the results showed that the dipole source inside the phantom could generate a magnetic field distribution on the scalp that was close to its theoretical values. The average source localization accuracy of 5.51 mm verified the effectiveness of the designed phantom and the performance of our OPM-MEG system. This work provides an effective test platform for OPM-MEG.