Source Localization Method Research Articles

Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment.

In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.

An Analytical Method for 3-D Sound Source Localization Based on a Five-Element Microphone Array

An analytical method for 3D sound source localization based on a five-element microphone array is proposed in this work. With four time-difference-of-arrival (TDOA) values, the proposed method realizes the sound source localization by analytical solution. A new five-element microphone array configuration (which is characterized in that four microphones are in a same plane and at four vertices of a concave quadrilateral respectively, while the fifth microphone is out of the plane) is analyzed and studied. It is proved that with the proposed configuration, the five-element microphone array can obtain a unique and analytical solution. To investigate the effectiveness and the localization performance of the proposed method, a practical five-element microphone array is developed and 3D sound source localization experiments are carried out. The TDOA values are obtained by the generalized cross correlation (GCC) method based on the phase transform (PHAT). The experimental results show that the proposed method is an effective near-field localization method. The localization performance of the practical five-element microphone array is satisfactory. Compared with the conventional methods, the proposed method realizes the 3D localization by analytical solution and has the advantages of simple construction and lower computational complexity.

Localization of Mixed Far-field and Near-field Incoherently Distributed Sources Using Two-stage RARE Estimator

In this paper, a mixed source localization method utilizing a two-stage rank-reduction (RARE) estimator is investigated. Different from the existing methods, the proposed one is built on the incoherently distributed (ID) source model, which is more appropriate for multipath and fast time-varying channels. Firstly, a general array manifold (GAM) model is established, where nominal DOAs and nominal ranges are extracted from the initial array manifold. By exploiting the shift invariance property of the far-field (FF) GAM and combining virtual source enumeration result, nominal FF DOA estimation is achieved by a one-dimensional (1-D) RARE spectral search. Secondly, the oblique projection operation is adopted to separate near-field (NF) sources, and the nominal DOA and range parameters of NF sources are subsequently obtained by jointly utilizing the manifold separation technique (MST) and another two 1-D spectral searches. With the estimated nominal DOA and range parameters, the angular spread and range spread are then successfully estimated. Moreover, the Cramér-Rao bound (CRB) for the considered case is also derived. Simulation results are presented to validate the effectiveness of the proposed method.

A 3D source localization method based on whale optimization algorithm: Experiments for locating a time-varying source in a dynamic indoor environment

To solve the problem of pollutant source localization in an indoor environment with dynamic mechanical ventilation better, this paper designed and built a 3D source localization system composed of three 3D source localization robots whose sensors can move under control in the height direction (0.5 m–1.5 m). Through adopting the 3D source localization system, we realized the application of the previously developed the improved whale optimization algorithm (IWOA) method in 3D source localization and proposed the 3D_IWOA method. To validate the feasibility of the 3D_IWOA method and compare the performance of the 3D_IWOA method and other three 3D source localization methods (the 3D_SPSO, 3D_WUⅡ, and 3D_IPSO methods), each method repeated 15 independent experiments in a Training-Center under the same conditions, respectively. For locating the periodic source at a height of 1.05 m in the downwind zone of this environment, the numbers of successful experiments of the 3D_SPSO, 3D_WUⅡ, 3D_IPSO, and 3D_IWOA methods were 5, 6, 11, and 13 (the success rates were 33.3%, 40.0%, 73.3%, and 86.7%), respectively. In addition, the means of total steps of the 3D_SPSO, 3D_WUⅡ, 3D_IPSO, and 3D_IWOA methods were 36.29 steps, 35.96 steps, 31.75 steps, and 38.15 steps, respectively.

Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

Pollution Source Localization in Complex Environments Using A Swarm of Mobile Agents

In this letter, we present a pollution source localization method combining particle swarm optimization (PSO) and artificial potential field (APF). PSO is used to control mobile agents to move toward the pollutant source according to concentration and position information. Meanwhile, to avoid collision during the movement, it is assumed that there exist repulsions around mobile agents and obstacles in monitoring area. APF is used to construct the corresponding repulsive force fields for each mobile agent. Besides, the residual energy is introduced to balance the energy consumption among mobile agents. Once the agent swarm locates the pollutant source, support vector regression is adopted to model the pollution diffusion based on concentration measurements and distribution of mobile agents. Experimental evaluations demonstrate the effectiveness and feasibility of our proposed method.

Drone Audition: Sound Source Localization Using On-Board Microphones

This paper presents a sound source localization method using an irregular microphone array embedded in a drone. Sound source localization is an integral function of drone audition systems which enables various applications of drones such as search and rescue missions. However, the audio recordings using the on-board microphones obscure the sound emitted by a source on the ground due to drone generated motor and propeller noise, thus leading to an extremely low signal-to-drone noise ratio (SdNR). In this paper, we propose a cross-correlation based direction of arrival (DOA) estimation technique using the time difference of arrival (TDOA) at different microphone pairs, with noise angular spectrum subtraction. Through the measured current-specific drone noise spectrum, noise suppression has been achieved from the multi-channel recordings. Experimental results show that the proposed method is capable of estimating the position in three-dimensional space for simultaneously active multiple sound sources on the ground at low SdNR conditions ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-30$</tex-math></inline-formula> dB), and localize two sound sources located at a certain azimuth angular separation with low prediction error comparable to the multiple signal classification (MUSIC) based algorithms and the generalized cross-correlation with phase transformation (GCC-PHAT) method. Due to its simplicity, applicability to any array geometry, and better robustness against drone noise, the proposed method increases the feasibility of localization under extreme SdNR levels.

Multiple Sound Source Localization in Three Dimensions Using Convolutional Neural Networks and Clustering Based Post-Processing

Sound source localization methods are successfully applied for various estimation tasks, such as tracking and detecting objects, aiming cameras, and navigating robots. However, large and usually complex distributed microphone arrays are used for three-dimensional acoustic source localization. This study proposes a convolutional neural network architecture for three-dimensional sound source localization using a single tetrahedral microphone array. A spectrum phase component of a microphone array signal was designed as the input of the model, while the output represents a three-dimensional space. The paper provides extensive experimental results of the given method on a semi-synthetic audio data set and a real-world microphone array. Furthermore, cluster-based post-processing has been shown to increase the accuracy of three-dimensional localization by more than 30%. The experimental results on a synthesized data set using the image source method showed 1.08 m localization uncertainties. The estimate of the investigated sound sources had a mean absolute error of 18.97° and elevation error of 48.49°. An additional advantage of the proposed method is the ability to predict the location of the sound source from a single signal analysis frame. This gives instant localization and is in line with many alternative applications. The proposed solution does not require intensive preprocessing of the audio signals and can be used as a video camera pointing system based on a microphone array. In the future, it would be relevant to investigate the localization performance of more than two sound sources, and the variable acoustic conditions could also be assessed.

Acoustic Source Localization in the Circular Harmonic Domain Using Deep Learning Architecture

The problem of direction of arrival (DOA) estima- tion with a circular microphone array has been addressed with classical source localization methods, such as the model-based methods and the parametric methods. These methods have an ad- vantage in estimating the DOAs in a blind manner, i.e. with no (or limited) prior knowledge about the sound sources. However, their performance tends to degrade rapidly in noisy and reverberant environments or in the presence of sensor array limitations, such as sensor gain and phase errors. In this paper, we present a new approach by leveraging the strength of a convolutional neural network (CNN)-based deep learning approach. In particular, we design new circular harmonic features that are frequency- invariant as inputs to the CNN architecture, so as to offer improvements in DOA estimation in unseen adverse environments and obtain good adaptation to array imperfections. To our knowledge, such a deep learning approach has not been used in the circular harmonic domain. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than the recent baseline methods, in a variety of noise and reverberation levels, in terms of the accuracy of the DOA estimation.

Photonics-assisted Wideband RF Source Localization Method Based on Synthetic Aperture Interferometric Detection

Angle of arrival (AOA) is one key parameter for passive signal source localization. However, with the increase in RF systems' instantaneous bandwidth and working frequency, it is a great challenge for traditional narrowband array signal processing methods to achieve effective AOA measurement of wideband RF sources. This paper proposes a photonics-assisted wideband RF source localization method based on synthetic aperture interferometer radiometer (SAIR) detection. The proposed method introduced cross-power spectrum (CPS), which can characterize the interference at different frequencies to define the wideband visibility function. Then the wideband SAIR detection is achieved from the wideband visibility function, which is Fourier transformed in the spatial domain and then performs integration in the frequency domain. Numerical simulation and proof-of-concept experiment results show that the proposed method can suppress the grating lobes caused by large element spacing. With a microwave photonic (MWP) channelizer and an antenna array, a wideband signal covering 16-20 GHz is detected. The AOA resolution of the system is 3.54&#x00B0;, and the power of the maximum grating lobe in the AOA measurement is suppressed by over 80&#x0025;.

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

Acoustic source localization in three-dimensional space based on acoustic valley-Hall topological insulators

Acoustic valley-Hall topological insulators (VHTIs), as a kind of acoustic metamaterials, possess the attractive characteristics of both directional sound reception and regulating the propagation path of sound wave. Based on these two characteristics of acoustic VHTIs, a VHTIs-based acoustic source localization method is developed here. Firstly, the topological valley transport in the acoustic VHTIs is investigated by emitting sound to the topological interface from the routes of the entrance of the topological interface and the domain wall. We find that sound waves emitting from the entrance of the topological interface and the domain wall into the topological interface possess different angular sensitivity. Furthermore, utilizing the different angular sensitivities of the sound propagation from the two routes to the topological interface, the thin films placed at the front of the entrance of the topological interface are designed to improve the directional sound reception to a targeted sound source. The simulated and experimental results demonstrate that the half-power beamwidth can be decreased by means of placement of the film. Finally, a method of acoustic source localization by using three acoustic VHTIs is proposed and verified via experiments.

A deep reinforcement learning based searching method for source localization

The localization of hazardous sources (e.g. poisonous gas sources) is an important task regarding the security of human society. To find the unknown source in time, various autonomous source searching methods have mushroomed and been employed over the past decade. This paper designs a fresh source searching approach, namely particle clustering-deep Q-network, PC-DQN, which applies the deep reinforcement learning (DRL) techniques as a source searching approach for the first time. Specifically, the search process is formulated as the partially observable Markov decision process, then converted into the Markov decision process based on the belief state (represented by the particle filter). PC-DQN leverages the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the feature of belief state, and employ the deep Q-network (DQN) algorithm to find the optimal policy for the source searching task. Through the comparison with two baseline methods (i.e. RANDOM and Entrotaxis algorithm) under various experimental conditions, the viability of our proposed PC-DQN is testified. Results explicitly reveal that the success rate of the PC-DQN maintains at a high level (beyond 99.6%) in all scenarios in this paper, and the mean search step shows evident superiority over baseline methods in most scenarios. Significantly, we also introduce the transfer learning concept to reuse the well-trained Q-network into new scenarios. These findings show important implications of the DRL-based approach as an alternative and more effective source searching approach.

A novel acoustic emission source location method for crack monitoring of orthotropic steel plates

As the fatigue problem of orthotropic steel plates becomes increasingly severe in medium- and long-span bridges, structural health monitoring of the plates that could identify cracks timely is in great demand. Considering that orthotropic steel plates are of multiple structural elements and complex geometries, it is challenging to locate the cracks accurately. To this end, a novel acoustic emission (AE) source location method based on empirical wavelet transform and long short-term memory neural network was developed for crack monitoring of orthotropic steel plates. Empirical wavelet transform adaptively decomposed prominent components contained in the raw AE signal, of which the one with distinct dispersive modes was selected as the characteristic component to more precisely determine the arrival time through Akiake information criteria. The long short-term memory model established a more exact relationship between the arrival time differences of AE signals at multiple sensors and the source coordinates. With the help of empirical wavelet transform and long short-term memory model, it significantly eliminated the influence of dispersion, reflection, scattering, multiple propagation paths and noise, and thus guaranteed higher crack location accuracy. Through field experiment on a long-span suspension bridge, where cracks were simulated by artificial AE sources, a comprehensive comparison was carried out among traditional time-of-arrival method, delta-T mapping method, improved delta-T mapping method based on Akiake information criteria and Gaussian process, a counterpart of the proposed method based on empirical wavelet transform and Gaussian process, as well as the proposed method based on empirical wavelet transform and long short-term memory neural network. The location errors and their variations of the proposed method were found far less than those of the other existing methods. The location accuracy could be further improved by increasing the grid resolution of pencil lead break pre-tests. The results demonstrated the feasibility and superiority of the proposed crack location method for field applications on large-scale complex structures like orthotropic steel plates.

A graph-based narrowband matched-field source localization method.

Matched field processing (MFP) has been regarded as one of the most successful acoustical methods for positioning underwater sources. In this paper, the narrowband MFP method is combined with a recently developed framework-the graph signal processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the graph-based MFP outperforms the conventional MFP processors for its better accuracy and fewer requirements for sensor numbers.

Beamforming for measurements under disturbed propagation conditions using numerically calculated Green’s functions

Beamforming methods for sound source localization are usually based on free-field Green’s functions to model the sound propagation between source and microphones. This assumption is known to be incorrect for many industrial applications and the beamforming results can suffer from this inconsistency regarding both, main lobe width and dynamic range. The aim of this paper is to investigate whether the use of numerically calculated Green’s functions, which include the diffraction and reflection of the sound path between source and microphones, can improve the results of beamforming measurements.The current test cases of numerical and experimental investigations consist of a source placed in a short rectangular duct. The measurements are performed outside the duct in a semi-anechoic chamber. A typical example for this kind of installation is a fan with a heat exchanger.The Green’s functions for this test case are calculated numerically using the boundary element method. These tailored Green’s functions are used to calculate the corresponding beamforming steering vectors.Beamforming measurements are performed in this paper using a loudspeaker mounted in a disc as a reference source in the heat exchanger duct. The measurements are performed both with stationary and rotating disc. The stationary measurements are evaluated in the frequency domain. For the evaluation of the rotating measurements, a new beamforming method in the time domain is presented. This method also uses the stationary Green’s functions, which were calculated numerically in the frequency domain. It is also shown how the weighting of these tailored Green’s functions can be done for time domain beamforming.By means of different validation criteria it can be shown that the results with the numerical calculated Green’s functions are improved compared to free field beamforming. This is true both in the stationary and rotating case.

Optimal Passive Source Localization for Acoustic Emissions.

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Probabilistic Inverse Method for Source Localization Applied to ETEX and the 2017 Case of Ru-106 including Analyses of Sensitivity to Measurement Data

In recent years, cases of unexplained, elevated levels of radioactive particles have demonstrated an increasing need for efficient and robust source localization methods. In this study, a Bayesian method for source localization is developed and applied to two cases. First, the method is validated against the European tracer experiment (ETEX) and then applied to the still unaccounted for release of Ru-106 in the fall of 2017. The ETEX dataset, however, differs significantly from the Ru-106 dataset with regard to time resolution and the distance from the release site to the nearest measurements. Therefore, sensitivity analyses are conducted in order to test the method’s sensitivity to these parameters. The analyses show that the resulting source localization depends on both the observed temporal resolution and the existence of sampling stations close to the source. However, the method is robust, in the sense that reducing the amount of information in the dataset merely reduces the accuracy, and hence, none of the results are contradictory. When applied to the Ru-106 case, the results indicate that the Southern Ural region is the most plausible release area, and, as hypothesized by other studies, that the Mayak nuclear facility is the most likely release location.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BackgroundElectrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. New methodsWe undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. ResultsPower envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn’t strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. ConclusionsThe competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Human mirror neuron system responsivity to unimodal and multimodal presentations of action.

This study aims to clarify unresolved questions from two earlier studies by McGarry et al. Exp Brain Res 218(4): 527-538, 2012 and Kaplan and Iacoboni Cogn Process 8: 103-113, 2007 on human mirror neuron system (hMNS) responsivity to multimodal presentations of actions. These questions are: (1) whether the two frontal areas originally identified by Kaplan and Iacoboni (ventral premotor cortex [vPMC] and inferior frontal gyrus [IFG]) are both part of the hMNS (i.e., do they respond to execution as well as observation), (2) whether both areas yield effects of biologicalness (biological, control) and modality (audio, visual, audiovisual), and (3) whether the vPMC is preferentially responsive to multimodal input. To resolve these questions about the hMNS, we replicated and extended McGarry et al.'s electroencephalography (EEG) study, while incorporating advanced source localization methods. Participants were asked to execute movements (ripping paper) as well as observe those movements across the same three modalities (audio, visual, and audiovisual), all while 64-channel EEG data was recorded. Two frontal sources consistent with those identified in prior studies showed mu event-related desynchronization (mu-ERD) under execution and observation conditions. These sources also showed a greater response to biological movement than to control stimuli as well as a distinct visual advantage, with greater responsivity to visual and audiovisual compared to audio conditions. Exploratory analyses of mu-ERD in the vPMC under visual and audiovisual observation conditions suggests that the hMNS tracks the magnitude of visual movement over time.