Source Localization Accuracy Research Articles

Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network

Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.

Acoustic Source Localization in a Reverberant Environment Based on Sound Field Morphological Component Analysis and Alternating Direction Method of Multipliers

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ( $T_{20}=174.30\,\,\text {ms}$ ). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise.

Sound Localization Bias and Error in Bimodal Listeners Improve Instantaneously When the Device Delay Mismatch Is Reduced.

Users of a cochlear implant (CI) in one ear, who are provided with a hearing aid (HA) in the contralateral ear, so-called bimodal listeners, are typically affected by a constant and relatively large interaural time delay offset due to differences in signal processing and differences in stimulation. For HA stimulation, the cochlear travelling wave delay is added to the processing delay, while for CI stimulation, the auditory nerve fibers are stimulated directly. In case of MED-EL CI systems in combination with different HA types, the CI stimulation precedes the acoustic HA stimulation by 3 to 10 ms. A self-designed, battery-powered, portable, and programmable delay line was applied to the CI to reduce the device delay mismatch in nine bimodal listeners. We used an A-B-B-A test design and determined if sound source localization improves when the device delay mismatch is reduced by delaying the CI stimulation by the HA processing delay (τHA). Results revealed that every subject in our group of nine bimodal listeners benefited from the approach. The root-mean-square error of sound localization improved significantly from 52.6° to 37.9°. The signed bias also improved significantly from 25.2° to 10.5°, with positive values indicating a bias toward the CI. Furthermore, two other delay values (τHA –1 ms and τHA +1 ms) were applied, and with the latter value, the signed bias was further reduced in some test subjects. We conclude that sound source localization accuracy in bimodal listeners improves instantaneously and sustainably when the device delay mismatch is reduced.

Indoor PDR Positioning Assisted by Acoustic Source Localization, and Pedestrian Movement Behavior Recognition, Using a Dual‐Microphone Smartphone

In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long‐term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed in this paper, which recognises the behavior of pedestrians for each gait and improves the stride length estimation for PDR localisation based on the recognition results to reduce the accumulation of errors in the PDR localisation algorithm itself. At the same time, the system uses self‐researched hardware to modify the audio equipment used for broadcasting within the indoor environment, to locate the acoustic source through a Hamming distance‐based localisation algorithm, and to correct the estimated acoustic source estimated location based on the known source location in order to eliminate the accumulated error in PDR localisation. Through analysis and experimental verification, the recognition accuracy of pedestrian movement behavior recognition proposed in this paper reaches 95% and the acoustic source localisation accuracy of 0.32 m during movement, thus, producing an excellent effect on eliminating the cumulative error of PDR localisation.

Multiple Acoustic Source Localization in Microphone Array Networks

The problem of multiple acoustic source localization using observations from a microphone array network is investigated in this article. Multiple source signals are assumed to be window-disjoint-orthogonal (WDO) on the time-frequency (TF) domain and time delay of arrival (TDOA) measurements are extracted at each TF bin. A Bayesian network model is then proposed to jointly assign the measurements to different sources and estimate the acoustic source locations. Considering that the WDO assumption is usually violated under reverberant and noisy environments, we construct a relational network by coding the distance information between the distributed microphone arrays such that adjacent arrays have higher probabilities of observing the same acoustic source, which is able to mitigate the miss detection issues in adverse environments. A Laplace approximate variational inference method is introduced to estimate the hidden variables in the proposed Bayesian network model. Both simulations and real data experiments are performed. The results show that our proposed method is able to achieve better source localization accuracy than existing methods.

Achieving 3D Beamforming by Non-Synchronous Microphone Array Measurements.

Beamforming technology is an essential method in acoustic imaging or reconstruction, which has been widely used in sound source localization and noise reduction. The beamforming algorithm can be described as all microphones in a plane simultaneously recording the source signal. The source position is then localized by maximizing the result of the beamformer. Evidence has shown that the accuracy of the sound source localization in a 2D plane can be improved by the non-synchronous measurements of moving the microphone array. In this paper, non-synchronous measurements are applied to 3D beamforming, in which the measurement array envelops the 3D sound source space to improve the resolution of the 3D space. The entire radiated object is covered better by a virtualized large or high-density microphone array, and the range of beamforming frequency is also expanded. The 3D imaging results are achieved in different ways: the conventional beamforming with a planar array, the non-synchronous measurements with orthogonal moving arrays, and the non-synchronous measurements with non-orthogonal moving arrays. The imaging results of the non-synchronous measurements are compared with the synchronous measurements and analyzed in detail. The number of microphones required for measurement is reduced compared with the synchronous measurement. The non-synchronous measurements with non-orthogonal moving arrays also have a good resolution in 3D source localization. The proposed approach is validated with a simulation and experiment.

Localization of multiple diffusion sources based on overlapping community detection

Localization of multiple diffusion sources is of great importance to various practical applications. However, accurately estimating the number of sources, which usually serves as the first step in this field, is still a challenging task. Most existing methods ignore the overlapping characteristic of the infected graph and thus hard to identify the appropriate infected partition for each diffusion source. Furthermore, these methods fail to fully utilize the contagion neighborhood of source candidates, resulting in poor accuracy of source localization. To overcome these problems, we propose a novel multiple sources detection method that utilizes both overlapping community detection and contagion neighborhood bias. In order to estimate the number of sources, the proposed method first utilizes the inherent peak–valley structure of the topological potential field to determine the number of sources. Then, it divides the infected graph into overlapping communities based on node position analysis in the topological potential field, which provides better partitions for source localization. Finally, it locates the single source in each partition based on likelihood estimation and contagion neighborhood bias which takes into account both infected and uninfected nodes in one’s neighborhood. In the experiment part, we evaluate our method on both real-world networks and synthetic networks with various scales and structural features. The results show that our proposed method not only estimates the number of sources more accurately but also locates each source more precisely, outperforming the existing state-of-the-art methods. In addition, our method gives stable performances on different kinds of synthetic networks, exhibiting good robustness.

A Sequential ESPRIT Algorithm Based on a Novel UCSA Configuration for Parametric Estimation of Two-Dimensional Incoherently Distributed Source

Two-dimensional distributed source is a more realistic model to represent the target signal propagation in such fields as radar, sonar, and wireless communication. This paper presents a novel uniform circular sub-array (UCSA) configuration, which can be used to accurately estimate parameters of multiple two-dimensional incoherently distributed (TDID) sources. By sequentially utilizing the mapping relationship between the generalized array manifold, which has the spatial translation invariant relationship, and the signal subspace of TDID source, a closed-form solution of four parameters (central and spread parameters within azimuth and elevation dimension) of every TDID source can be obtained with low computational complexity. Furthermore, by properly using the signal subspace, the estimation accuracy is improved significantly. Numerical simulations illustrate the properties of the proposed method and show that it can simultaneously improve the robustness with respect to the scattering size and uncertainty of the spatial distribution. The proposed method has the potential to be used for fast and accurate multi-TDID source localization with arrays of small aperture and few array elements.

Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter

Background and objective: The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures. The reconstruction of subcortical sources is a challenging task because the signal from these sources is weakened and mixed with artifacts and other signals from cortical sources. In this proof-of-principle study we present a novel EEG source imaging method, the regional spatiotemporal Kalman filter (RSTKF), that can detect deep brain activity.Methods: The regional spatiotemporal Kalman filter (RSTKF) is a generalization of the spatiotemporal Kalman filter (STKF), which allows for the characterization of different regional dynamics in the brain. It is based on state-space modeling with spatially heterogeneous dynamical noise variances, since models with spatial and temporal homogeneity fail to describe the dynamical complexity of brain activity. First, RSTKF is tested using simulated EEG data from sources in the frontal lobe, putamen, and thalamus. After that, it is applied to non-averaged interictal epileptic spikes from a presurgical epilepsy patient with focal epileptic activity in the amygdalo-hippocampal complex. The results of RSTKF are compared to those of low-resolution brain electromagnetic tomography (LORETA) and of standard STKF.Results: Only RSTKF is successful in consistently and accurately localizing the sources in deep brain regions. Additionally, RSTKF shows improved spatial resolution compared to LORETA and STKF.Conclusions: RSTKF is a generalization of STKF that allows for accurate, focal, and consistent localization of sources, especially in the deeper brain areas. In contrast to standard source imaging methods, RSTKF may find application in the localization of the epileptogenic zone in deeper brain structures, such as mesial frontal and temporal lobe epilepsies, especially in EEG recordings for which no reliable averaged spike shape can be obtained due to lack of the necessary number of spikes required to reach a certain signal-to-noise ratio level after averaging.

Speech intelligibility in noise with varying spatial acoustics under Ambisonics-based sound reproduction system

The present study investigates the effect of noise on speech intelligibility under an Ambisonics-based sound reproduction system, which is a commonly used technique for realising audio virtual reality. The sound reproduction system was built in an anechoic chamber using a 16 channel spherical loudspeaker array. A commercially available first-order Ambisonics microphone array was utilised for collecting room impulse responses of rooms with different acoustic characteristics, and a decoder for generating signals rendered from the loudspeaker array. A subjective listening test was conducted in the sound reproduction system examining how intelligibility of sentences is compromised by noise under virtually reproduced acoustics environments with various acoustical conditions. Particular focus was given to investigating the effect of the amount of reverberation in rooms as well as the angular separation between the target speech and noise sources, both of which are known to have a significant effect on speech intelligibility in real (i.e. non-virtual) acoustic environment.The experimental results suggest that the intelligibility of speech masked by pink noise was significantly reduced under reverberant acoustical environments reproduced by the Ambisonics-based sound reproduction system compared to the baseline anechoic environment. However, only marginal differences were observed between the acoustical environments with different reverberation time. The effect of spatial release from masking was also observed under the reproduced acoustic environments and showed some correlation with the accuracy of source localisation realised by the sound reproduction system.

A Novel Bayesian Approach for EEG Source Localization.

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

Construction and validation of a database of head models for functional imaging of the neonatal brain.

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age‐specific head models. Although an age‐appropriate population‐level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual‐level datasets at ages 29–44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra‐cerebral tissue against manual segmentation. We have also conducted a leave‐one‐out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best‐matching individual from the database in place of a subject‐specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates

Objective. Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model. Approach. We performed numerical simulations to investigate source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method. Main results. Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source localization errors for sources located in areas beneath the fontanels. Our finding clearly shows that the CSF inclusion and GM/WM distinction in EEG inverse modeling can substantially reduce EEG source localization errors. Moreover, fontanels should be included in neonatal head models, particularly in source localization applications, in which sources of interest are located beneath or in vicinity of fontanels. Significance. Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.

Microseismic source location in tunnels: Effect of arrival-time picking

The microseismic (MS) monitoring technique is widely used in deep tunnels for rock mass stability monitoring and disaster warning. MS source location is the basis of the MS monitoring technique. This study investigates the effect of arrival-time picking on MS source location in a tunnel. A total of 1000 MS sources, 12,000 arrival-time pickings, and particle swarm optimization are used. Accordingly, 3796 source-locating operations are conducted for each MS source. The results show that the number of arrival-time pickings and their mode of combination significantly affect the accuracy and the stability of MS source location in a tunnel. The optimal location cannot be obtained when using the arrival-time pickings with the smallest errors. The optimal location accuracy decreases with the increase in the number of arrival-time pickings; however, the average location accuracy increases therewith. The source location accuracy significantly changes when the number of arrival-time pickings is small. The location result becomes more stable with the increasing number of arrival-time pickings. Accordingly, a suggestion involving the usage of all arrival-time pickings for MS source location in a tunnel is provided. The results will contribute to rock mass stability monitoring and disaster warning in tunnels using the MS monitoring technique.

Acoustic source localization with the angular spectrum approach in continuously stratified media.

The angular spectrum approach (ASA)-a frequency domain method to calculate the acoustic field-enables highly efficient passive source localization and modeling forward propagation in homogeneous media. If the medium is continuously stratified, a first-order analytical solution may be obtained for the field at arbitrary depth. Simulations show that the proposed stratified ASA solution enables accurate source localization as compared to the uncorrected ASA (error from 1.2 ± 0.3 to 0.49 ± 0.3 wavelengths) at scalings relevant to biomedical, underwater, and atmospheric acoustic applications, and requiring milliseconds on nonspecialized hardware. The results suggest the proposed correction enables efficient and accurate localization in stratified environments.

Localization of Partial Discharge in Electrical Transformer Considering Multimedia Refraction and Diffraction

Partial discharge (PD) is a widely adopted method for the internal insulation detection of electrical transformers. The impact of the refraction and diffraction of the ultrasonic signal in the oil, winding, and core in the transformer makes the localization of the PD source inside the transformer a nontrivial task. The refraction and diffraction factors can introduce additional complexity to the localization equations that can hardly be solved. This article proposes an algorithmic solution based on the time difference of arrival algorithm and semidefinite relaxation convex optimization to improve the PD source localization accuracy. The refraction and diffraction error, measurement error, and their relationship in the process of PD signal propagation are fully considered. The proposed solution is extensively assessed through simulation, testbed, and field experiments, and the results confirm that the proposed solution outperforms the Chan and particle swarm optimization algorithm with the localization error of about 0.1 m.

Acoustic Emission Wave Velocity Attenuation of Concrete and Its Application in Crack Localization

The accurate localization of an acoustic emission (AE) source is a vital aspect of AE nondestructive testing technology. A model of wave velocity attenuation caused by the extension of transmission distance is established to analyze the attenuation of AE wave velocities in concrete and thus improve the acoustic source localization accuracy from the perspective of modified velocity. In combination with the exhaustive and region localization methods, a region exhaustive localization method is established based on the modified wave velocity. The results indicate that the smaller the water–cement ratio, the larger the reference wave velocity, and the spatially dependent attenuation of wave velocity increase. Moreover, the larger the aggregate particle size, the larger the reference wave velocity, and the greater the attenuation of wave velocity with distance. For a propagation distance of 1000 mm, the AE wave velocity attenuation exceeds 50% compared with the AE velocity. The optimized localization method reduces the number of nodes calculated, thus improving the method’s accuracy when used for localization.

The Influence Mechanism and Optimization of the Sensor Network on the MS/AE Source Location

The sensor network layout is a key factor affecting the accuracy and stability of the microseismic/acoustic source (MS/AE) location. Based on the arrival-time-difference principle, the hyperbolic/hyperboloidal governing equations for the source location are derived. The nonuniformity geometrical characteristics of hyperbolic/hyperboloidal field for the source location are obtained. The sensor network does not induce any location errors; it only affects the source location accuracy by amplifying the existing errors in the input data during the source location process. Also, this amplication effect of the input data errors is characterized by nonuniformity because of the nonuniformity of the hyperbolic/hyperboloidal field. Furthermore, two basic effects, the geometrical spreading and the directional control, of the sensor network are investigated, and the three-dimensional space quantitative models of these two effects are established, respectively. The influence of the wave velocity error and arrival time error on the source location accuracy is analytically compared, and the propagation characteristics of these two types of errors during the source location process are revealed. The concepts of critical arrival-time difference and critical hyperbola/hyperboloid are proposed. Based on these two concepts, the monitoring area can be divided into two regions where the source location accuracy is controlled by the velocity error and the arrival time error, respectively. The concept of direction angle of paired sensors is proposed, and the relationship between the source location and the layout of four typical paired sensors is studied. Finally, the principles of sensor network optimization are determined.

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our “ground truth” for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. Significance StatementDeep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.

Rapid localization of acoustic source using sensor clusters in 3D homogeneous and heterogeneous structures

The acoustic source localization in 3D structures is a challenging task especially if the structure is heterogeneous. A large number of unknown parameters in a 3D heterogeneous structure require a large number of sensor arrays for accurate source localization. In this article, a localization technique is proposed using triangular pyramid shaped sensor clusters. The time difference of arrival of acoustic signals between different sensors of the clusters is analyzed to localize the acoustic source. It uses acoustic wave propagation information to rapidly localize the acoustic source. This rapid acoustic source localization technique works well for both homogeneous and heterogeneous media. It considers the refraction in heterogeneous media using Snell’s law. The proposed technique is verified experimentally and numerically. The experimental results show that the technique is effective for source localization in 3D homogeneous structures. Numerical results are generated by finite element modeling for both homogeneous and heterogeneous structures. The results show the reliability of the proposed technique. This technique helps to localize the acoustic source with only a few sensors and is indispensable for monitoring large 3D structures with monitoring equipment that can handle only a few sensors.