Localization Of Multiple Sources Research Articles

Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization

Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of this problem, such as limited information, interactions among sources, and dependence on diffusion models, pose challenges to resolution. Current methods typically utilize heuristics and greedy selection, and they are usually bonded with one diffusion model. Consequently, their effectiveness is constrained. To address these limitations, we propose a simulation-based method termed BOSouL. Bayesian optimization (BO) is adopted to approximate the results for its sample efficiency. A surrogate function models uncertainty from the limited information. It takes sets of nodes as the input instead of individual nodes. BOSouL can incorporate any diffusion model in the data acquisition process through simulations. Empirical studies demonstrate that its performance is robust across graph structures and diffusion models. The code is available at https://github.com/XGraph-Team/BOSouL.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

Light field microscopy is a high-speed 3D imaging technique that records the light field from multiple angles by the microlens array(MLA), thus allowing us to obtain information about the light source from a single image only. For the fundamental problem of neuron localization, we improve the method of combining depth-dependent dictionary with sparse coding in this paper. In order to obtain higher localization accuracy and good noise immunity, we propose an inertial proximal gradient acceleration algorithm with dry friction, Fast-IPGDF. By preventing falling into a local minimum, our algorithm achieves better convergence and converges quite fast, which improves the speed and accuracy of obtaining the locolization of the light source based on the matching depth of epipolar plane images (EPI). We demonstrate the effectiveness of the algorithm for localizing non-scattered fluorescent beads in both noisy and non-noisy environments. The experimental results show that our method can achieve simultaneous localization of multiple point sources and effective localization in noisy environments. Compared to existing studies, our method shows significant improvements in both localization accuracy and speed.

Multiple Source Localization in IoT: A Conditional GAN and Image-Processing-Based Framework

Multiple Source Localization in IoT: A Conditional GAN and Image-Processing-Based Framework

A physics-informed artificial fish swarm algorithm for multiple tunnel fire source locations prediction

To address multiple tunnel fire source locations prediction for better firefighting, this work aims to establish a model-driven and data-driven fusion algorithm, which is named as the physics-informed artificial fish swarm algorithm. In the algorithm, the artificial fish swarm algorithm is utilized to avoid complex fire mechanisms modelling, which makes the algorithm owns the advantage of the data-driven methods. Meanwhile, a physical model of the longitudinal ceiling temperature distribution relative to fire source locations and some physical mechanisms are utilized to guide the data-driven artificial fish swarm algorithm for converging to the reasonable prediction, which makes the algorithm owns the advantage of the model-driven methods. Two full-scale tunnel fire experiments with two pool fire sources and three pool fire sources as well as one full-scale cable fire experiment in the tunnel are utilized to support and verify the algorithm, which can be utilized to achieve multiple tunnel fire sources prediction and ceiling temperature distribution prediction in tunnel fires.

High-Resolution Detection, Localization, and Classification of Multiple Magnetic Dipole Sources

High-Resolution Detection, Localization, and Classification of Multiple Magnetic Dipole Sources

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Passive localization of multiple sources while the receiver is in motion is a fundamental problem in wireless communication with practical applications in various fields such as acoustic event localization and object tracking. In this study, we propose a novel method for three dimensional (3D) angle of arrival (AOA) localization using a moving receiver with a limited number of sensors. In the proposed method, we record signals from sources in a series of time windows. Then we estimate the elevation and azimuth AOAs of sources along with their pairing by using the proposed three low-complexity deep learning (DL)-based AOA estimators. Following the initial AOA estimation, a low-complexity off-grid based refinement technique is employed to further enhance the accuracy of the estimated elevation and azimuth AOAs based on the maximum likelihood (ML) criterion. Next, a multi-source 3D-localization algorithm is proposed to estimate the positions of sources across the recorded time windows. The simulation results validate the effectiveness and efficiency of the proposed method. The results highlight that the DL-based AOA and location estimators outperform recent studies, while also demonstrating robustness in the face of practical imperfections, such as situations where the receiver’s position or direction is uncertain during movement. Our complexity analysis verifies that the proposed method outperforms existing methods in terms of speed and the number of computations. This enhanced computational efficiency makes the proposed method highly attractive and practically significant for application in various fields.

Counting and Localization of Multiple Unknown Harmonic Sound Sources in Entire 3D Space Using Just Five Microphones

Counting and localization of multiple sound sources in an unknown environment has many applications, namely fault diagnosis of complex machineries, hearing aids' development, and localization of enemy vehicles in defense. However, multiple sound source localization is extremely challenging because it requires numerous microphones, which increases the cost, computational time, and installation difficulties. Here, we present a new method to count the number of stationary harmonic sound sources and localize them in a free-field environment using a novel oblique square pyramid shaped array of only 5 microphones. We find FFT peaks of microphone signals and compare the ratio of the magnitude of the most dominant peak with remaining peaks' one-by-one. If these ratios are below 25 dB difference, they are considered as sound source and counted. A new time delay and intensity ratio computation is applied to the segregated source signals to calculate exact coordinates of each source. Simulation studies of four concurrent sources show that our method predicts all sources' coordinates at every octant location with mean error of 3.4%, and less than 5% error beyond 20 dB Signal-to-Noise ratio. Mean computational time is 0.92 second. Therefore, our method is low-cost, quicker, more accurate and more reliable than existing techniques.

Multiple sources localization with 2D-DFT under distributed massive antenna arrays

Traditional localization approaches, e.g., angle of arrival clustering (AOA-clustering), subspace data fusion, and minimum variance distortionless response, entail eigenvalue decomposition or global grid search within the target area, which could pose significant challenges when applied to massive antenna arrays as a result of their high computational complexity. In this paper, we present a low-complexity localization approach for multiple sources with two-dimensional discrete Fourier transform (2D-DFT). Firstly, we compute the cross-covariance, which could effectively suppress noise and obtain aligned AOAs via 2D-DFT. Then, we utilize phase offset method and total least square solution to obtain accurate position estimates. The proposed approach could achieve a good compromise between performance and computational complexity. The Cramér-Rao Bound (CRB) and simulation results are provided to corroborate the effectiveness and superiority of the proposed approach.

Collaborative robotics and adaptive machines laboratory – research advances in autonomous systems, advanced manufacturing, and healthcare

This report summarizes the recent research projects carried out at the Collaborative Robotics and Adaptive Machines Laboratory, Old Dominion University. The topics include collaborative robotics strategies for non-repetitive micro-drilling tasks, hand-held robots for spillage-free specimen retrieval tasks, development of a three-dimensional simulator for autonomous underwater vehicles, and enhancing search efficiency of glowworm-inspired robotic swarms for multiple source localization tasks. Research in these areas has applications in autonomous systems, advanced manufacturing, and health care.

LaSNet: An end-to-end network based on steering vector filter for sound source localization and separation

In this paper, we propose a novel time-domain end-to-end network (LaSNet) for solving the problem of multiple sound sources localization (SSL) and separation based on microphone array. The traditional time-frequency (T-F) signal representation is subject to various prior conditions and fails to separate the different sound signal components. Even the data-driven neural network does not develop an effectively integrated approach where localization and separation interplay to serve both challenge. To address the aforementioned issue, we propose a novel approach that involves the implementation of a Separation Driving Localization Network (SDLNet). This framework operates by extracting latent features from a separation network and subsequently employing them in the context of a localization network. Then we propose a simple multi-task network for both SSL and separation. Through the analysis of steering vector filter, we find that the localization and separation problems can be linked by the operation of pseudo-inverse (pinv). To facilitate a synergistic relationship between SSL and sound separation, while also enabling end-to-end network training, we develop a Pinv Module (PM). Fianlly, the Localization and Separation Network (LaSNet) structure of this paper is proposed. Inspired by the overlay mechanism of network, LasNet is extended to a multi-task and multi-layer network, in which separation task is divided into multiple subtasks. A fuzzy separation loss function is introduced for training multi-layer network. Numerical experiments demonstrate that the proposed method has a clearly better advantageous improvement than several well known models. LaSNet has greatly performance improvement in both separation and localization, and achieves at least 32% relative reduction in model size, compared with the baseline models.

Three-dimensional sound source diagnostic using a spherical microphone array from multiple capture positions

Most sound source diagnosis systems based on acoustic images are spatially stationary and unable to combine information from multiple capture positions, resulting in unreliable and partial diagnostics. In this paper it is proposed, developed and validated a measurement method based on a mobile spherical microphone array that generates acoustic images using beamforming from multiple capture positions, called Acoustic Imaging Structure From Motion (AISFM). The tridimensional localization, magnitude and directivity of multiple sound sources may be recovered with or without knowledge of the capture positions. An automatic and unsupervised acoustic image processing algorithm is deployed to facilitate the measurement workflow. An acoustic image correspondence problem is solved in order to eliminate false-positives and false-negatives from the diagnostic and render the AISFM feasible. Sound sources localization uncertainties estimates related to the sources’ magnitude, directivity and extension projected on the acoustic images are used in order to improve the diagnosis. The method and its effectiveness are experimentally validated with a measurement setup comprising five simultaneous sound sources on an anechoic environment.

Dimension decomposition algorithm for multiple source localization using uniform circular array

A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that the far-field signal can be considered as the state where the range parameter of the near-field signal is infinite, the algorithm for the near-field source localization is also suitable for estimating the direction of arrival (DOA) of far-field signals. By decomposing the first and second exponent term of the steering vector, the three-dimensional (3-D) parameter is transformed into two-dimensional (2-D) and one-dimensional (1-D) parameter estimation. First, by partitioning the received data, we exploit propagator to acquire the noise subspace. Next, the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA. At last, the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector, and the least squares (LS) is performed to acquire the range parameters. In comparison to the existing algorithms, the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum, which can achieve satisfactory localization and reduce computational complexity. Simulations are implemented to illustrate the advantages of the proposed DIDE method. Moreover, simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.

A Coherent Wideband Acoustic Source Localization Using a Uniform Circular Array.

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

A Comparison of Multiple Odor Source Localization Algorithms

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data.

In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources' inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT2 angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters' search space and combine the differential evolution (DE) algorithm with the Levenberg-Marquardt (LM) algorithm to solve the sources' locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT2 angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.

Inverse identification model for release rates of multiple gaseous pollution sources in an aircraft cabin

When gaseous pollutants are airborne in an aircraft cabin, it is important to know the release rate of pollution sources efficiently and accurately for effective mitigation. At present, it is possible to locate single or multiple pollution sources using the sensor information, but there is no fast and accurate method to determine the strength of multiple pollution sources. In this study, using the monitored pollutant concentrations, with the assumption that the location of multiple pollution sources in an aircraft cabin is known, an inverse model for determining the release rates was established. In order to improve the calculation efficiency, the cause-effect matrix between the release rates of pollution sources and the concentrations of monitored points was obtained by introducing the contribution ratio of pollutant sources (CRPS) method. Due to the direct inversion of the cause-effect matrix being ill-posed, the Tikhonov regularization method was used to enhance the stability of the inverse solution. The inverse model was validated by experiment in a three-dimensional cavity with CO2 as a tracer gas. The method was further demonstrated in a three-dimensional aircraft cabin by simulated data. The results show that the inverse modelling can accurately and efficiently quantify release rates of multiple pollution sources.

Disambiguation of measurements for multiple acoustic source localization using deep multi-dimensional assignments

The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usually known, generally termed the data association problem. Existing techniques generally formulate the data association problem as a multidimensional assignment and solve them using classical optimal techniques. Although these approaches show effectiveness for a limited amount of active sources, the computational time and complexity of these techniques increase significantly when the microphones or acoustic sources increase. In this paper, a learning approach is proposed that solves the multidimensional assignment using a deep neural network. Initially, the features that are employed for the association are formulated for each of the detected sources in all the array nodes, and a multidimensional assignment problem is formulated. Subsequently, a deep multidimensional assignment network is devised to extract the correspondence probability of the measurements received from the microphone arrays. Specifically, a data-driven differentiable approach is presented for multidimensional assignments that is computationally efficient. The proposed methodology is validated under realistic conditions for both speech and urban signals. The methodology is compared to state-of-the-art methods for showing the performance gain in terms of accuracy of association and location estimation with a reduced computational time.

Three-dimensional inversion of airborne time-domain electromagnetic data for ground sources

While most airborne time-domain electromagnetic (ATEM) surveys are carried out using moving-source systems, semi-airborne systems that use a ground-based source, such as GREATEM, have gained popularity in recent years because they allow for more in-depth exploration than moving-source systems. We presented a three-dimensional inversion method for interpreting transient data collected in semi-airborne surveys. Our method is based on a Gauss–Newton minimization approach. The forward problem is solved in the frequency domain using a secondary-field finite-difference technique, and the resulting solution and sensitivity are Fourier-transformed to the time domain using a digital filter. The sensitivities are evaluated by the adjoint-equation method, except for those of a surface region immediately below the source, which are derived by forward modeling the response of a perturbed model based on finite differences. We tested our inversion method on synthetic and real data. The synthetic tests show that the resolution capability of the semi-airborne surveys is dependent on the location of the ground source, which suggests that it is desirable to employ multiple source locations and perform joint inversion of all data sets to ensure the reliability of semi-airborne surveys. Finally, the real data example demonstrates that the recovered conductive zone is consistent with the known distribution of the mineralized zone.

Deep Attention Aware Feature Learning for Data Association in Multiple Source Localization

The letter addresses the problem of data association for multiple source localization in a distributed sensor network. To increase the efficiency of localization, we present a learning methodology to generate highly distinctive measurement features for data association. We propose an attention-based graph neural network that exploits the relationship among the phases from the Short Time Fourier transform and the estimated measurements to generate highly descriptive features for each of the detected source signals. The methodology is free from any selective threshold and processes each of the measurements to detect and enrich the similar and distinguishable aspects of the measurements. These aspects are then aggregated in feature representations for each source measurement. The generated features can then be employed with any data association method to segregate the measurements belonging to the same source. It is observed in experimental evaluation that the proposed features employed with state-of-the-art association frameworks yield lower association and localization errors.

Inverse estimation of multiple contaminant sources in three-dimensional heterogeneous aquifers with variable-density flows

Groundwater contamination is an exacerbating global issue that severely threatens human health. Subsurface remediation thus has gained increased interest in recent years. To effectively remediate subsurface contaminated sites, one needs to identify the locations of contaminant sources and characterize contaminant spreading. Groundwater contamination is often driven by multiple contaminant sources, making source identification problems challenging. Further, when the densities of dissolved contaminants and ambient groundwater differ, the resultant variable-density flows add complexity to the task of source identification. However, most previous studies are limited to a single source identification in a two-dimensional aquifer. This study presents a novel inversion method based on ensemble smoothing that identifies the locations of multiple contaminant sources in three-dimensional heterogeneous aquifer systems. A new covariance localization algorithm based on a clustering method is integrated into the inversion method, which improves the accuracy of multi-source identification. Using the proposed inversion framework, we successfully estimate the locations of multiple contaminant sources and three-dimensional permeability fields utilizing pressure and concentration data from monitoring wells. Further, we investigate and elucidate the effects of aquifer heterogeneity and variable-density flows on multiple source identification. We find that variable-density flow increases the data information contents and thus improves the inversion accuracy. This is the first-time demonstration of the effects of variable-density flow on the inversion accuracy of multi-source identification in three-dimensional heterogeneous aquifer systems.