Localization Of Multiple Sources Research Articles

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.

Adaptively accelerating FWM2DA seismic modelling program on multi-core CPU and GPU architectures

This paper presents work done towards porting of FWM2DA, an open source program, on multi-core CPU and GPU architectures. FWM2DA is a Fortran90 sequential program which performs acoustic wave propagation of single source location for the 2D subsurface earth model using finite difference time domain modelling. We have reproduced this program using C programming language and upgraded its functionality for performing acoustic wave propagation of multiple source locations and allowing different grid spacing in x and z direction for the subsurface earth model. Performance of the upgraded version is improved by implementing inter-node and intra-node parallelization. Inter-node parallelization is implemented using MPI for efficiently utilizing the distributed memory while intra-node parallelization focuses on efficient utilization of underlying architecture’s resources. Outcome of these programs are compared with that of the original FWM2DA program for Sigsbee2a model and found similar thus establishing the correctness of implementation done. The developed programs are tested using two layer subsurface earth model of 5000 X 5000 grid dimension on PARAM Shreshta system having Intel Xeon Gold 6148F Skylake CPU and NVIDIA Tesla V100 GPU. Ported programming models are evaluated for execution time for wavefield propagation of single source location using two layer subsurface earth model. Performance gain of 8.6X for OpenMP, 83.22X for OpenACC and 107.77X for CUDA C implementation are recorded over the sequential C program. The multi-core CPU program is further optimized and overall 29.37X performance is achieved with respect to the sequential C program. Performance of CUDA C program is also improved by making use of shared memory in GPU and 1.18X speedup is recorded with respect to the baseline CUDA C program. The numerical experiments demonstrate the effectiveness and robustness of the developed programs with high scalability and efficiency on multi-core CPU and GPU based HPC system.

Green's function retrieval and frequency-wavenumber methods for multiple source localization

In this work, Green's function retrieval and frequency-wavenumber methods are employed for multiple source localization in an outdoor environment. The Green's function retrieval methods are used to improve the signal-to-noise ratio of the beamforming maps. The frequency-wavenumber method is adapted to the plane-wave beamforming map to accurately localize real sources, while removing the appearance of ghost sources due to the data-association problem and mitigating missed detections for sources close together. Open field microphone array measurements of active and passive sources are investigated.

Locating multiple information sources in social networks based on the naming game

Identifying the source of information in a network plays a key role in controlling the impact of information. Herein, we study the problem of multiple source localization in the context of information propagation in social networks. We use the theory of the naming game to conduct observations. Moreover, we divide the observations into different sets based on the information provided by them and then estimate the source of each set. Finally, we combine the source of each observation set to obtain all the estimated information sources. The proposed method can locate sources without knowing the number of information sources. Simulations on four real data sets are provided to verify the performance of our method.

Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

A multiple sound source localization and counting method based on an angular spectrum is proposed in this paper. Local signal-to-noise ratio tracking, onset detection, and a coherence test are introduced to filter the generalized cross-correlation angular spectrum in the time-frequency domain for multiple sound source localization and counting in noisy and reverberant environments. Then, dual-width matching pursuit is introduced to replace peak search as the method of localization and counting. A comprehensive comparison of two statistical indicators, mean precision and mean absolute estimated error, indicates that the proposed localization and counting algorithm using both the filtered angular spectrum and dual-width matching pursuit method is more robust and accurate than the classic counterpart, especially in environments with low signal-to-noise ratio, strong reverberation, and abundant sound sources.

A TDOA-based multiple source localization using delay density maps

The higher computational efficiency of the time difference of arrival (TDOA) based sound source localization makes it a preferred choice over steered response power (SRP) methods in real-time applications. However, unlike SRP, its implementation for multiple source localization (MSL) is not straight forward. It includes challenges as accurate feature extraction in unfavourable acoustic conditions, association ambiguity involved in mapping the feature extractions to the corresponding sources and complexity involved in solving the hyperbolic delay equation to estimate the source coordinates. Moreover, the dominating source and early reverberation make the detection of delay associated with the submissive sources further perplexing. Hence, this paper proposes a proficient three-step method for localizing multiple sources from delay estimates. In step 1, the search space region is partitioned into cubic subvolumes, and the delay bound associated with each one is computed. Hereafter, these subvolumes are grouped differently, such that whose associated TDOA bounds are enclosed by a specific delay interval, are clustered together. In step 2, initially, the delay segments and later each subvolume contained by the corresponding delay segment are traced for passing through estimated delay hyperbola. These traced volumes are updated by the weight to measure the likelihood of a source in it. The resultant generates the delay density map in the search space. In the final step, localization enhancement is carried out in the selected volumes using conventional SRP (C-SRP). The validation of the proposed approach is done by carrying out the experiments under different acoustic conditions on the synthesized data and, recordings from SMARD & Audio Visual 16.3 Corpus.

Multi-unmanned aerial vehicle multi acoustic source localization

This paper addresses a multisource localization problem with multiple unmanned aerial vehicles equipped with appropriate sensors coordinating with each other, wherein the sources are simultaneously emitting identical acoustic signals. Distributed coordinated localization algorithms based on multiple range and direction measurements are presented and performances are evaluated in different practically significant mission scenarios. Non-deterministic polynomial (NP) hardness to determine optimal number of unmanned aerial vehicles for a given mission scenario is discussed. Group coordination, tactical path, and goal replan strategies to enable efficient localization of single and multiple acoustic sources have been designed. The localization algorithm along with coordination strategy is verified in the presence of realistic error conditions through simulation.

Localization of multiple sources with known waveforms by array response matching

We present a novel and computationally-efficient solution to the localization of multiple sources with known waveforms, based on array response matching. We first derive the multidimensional array response matching (ARM) solution, which amounts to a minimization problem involving the location of the sources. To simplify the computational complexity, we derive a 1-dimensional version, which is closely related to the MVDR solution. We then introduce a linearly constrained version of this solution and apply it to exploit a-priori knowledge regarding the waveforms of the sources. The proposed solution involves only a 1-dimensional search and is therefore computationally much simpler than the corresponding maximum likelihood (ML) solution, which involves an iterative multidimensional search. Simulation results, demonstrating the close similarity in performance of the proposed solution and the ML solution, are included.

A Lagrangian Multiplier Method for TDOA and FDOA Positioning of Multiple Disjoint Sources with Distance and Velocity Correlation Constraints

This paper considers the source localization problem using time differences of arrival (TDOA) and frequency differences of arrival (FDOA) for multiple disjoint sources moving together with constraints on their distances and velocity correlation. To make full use of the synergistic improvement of multiple source localization, the constraints on all sources are combined together to obtain the optimal result. Unlike the existing methods that can achieve the normal Cramér-Rao lower bound (CRLB), our object is to further improve the accuracy of the estimation with constraints. On the basis of maximum likelihood criteria, a Lagrangian estimator is developed to solve the constrained optimization problem by iterative algorithm. Specifically, by transforming the inequality constraints into exponential functions, Lagrangian multipliers can be used to determine the source locations via Newton’s method. In addition, the constrained CRLB for source localization with distance and velocity correlation constraints is also derived. The estimated accuracy of the source positions and velocities is shown to achieve the constrained CRLB. Simulations are included to confirm the advantages of the proposed method over the existing methods.

Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

3D Multiple Sound Source Localization by Proposed Cuboids Nested Microphone Array in Combination with Adaptive Wavelet-Based Subband GEVD

Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate the number of speakers, along with the speaker’s location, and with high accuracy is required in real-time conditions. The spatial aliasing is an undesirable effect of the use of microphone arrays, which decreases the accuracy of localization algorithms in noisy and reverberant conditions. In this article, a cuboids nested microphone array (CuNMA) is first proposed for eliminating the spatial aliasing. The CuNMA is designed to receive the speech signal of all speakers in different directions. In addition, the inter-microphone distance is adjusted for considering enough microphone pairs for each subarray, which prepares appropriate information for 3D sound source localization. Subsequently, a speech spectral estimation method is considered for evaluating the speech spectrum components. The suitable spectrum components are selected and the undesirable components are denied in the localization process. The speech information is different in frequency bands. Therefore, the adaptive wavelet transform is used for subband processing in the proposed algorithm. The generalized eigenvalue decomposition (GEVD) method is implemented in sub-bands on all nested microphone pairs, and the probability density function (PDF) is calculated for estimating the direction of arrival (DOA) in different sub-bands and continuing frames. The proper PDFs are selected by thresholding on the standard deviation (SD) of the estimated DOAs and the rest are eliminated. This process is repeated on time frames to extract the best DOAs. Finally, K-means clustering and silhouette criteria are considered for DOAs classification in order to estimate the number of clusters (speakers) and the related DOAs. All DOAs in each cluster are intersected for estimating the position of the 3D speakers. The closest point to all DOA planes is selected as a speaker position. The proposed method is compared with a hierarchical grid (HiGRID), perpendicular cross-spectra fusion (PCSF), time-frequency wise spatial spectrum clustering (TF-wise SSC), and spectral source model-deep neural network (SSM-DNN) algorithms based on the accuracy and computational complexity of real and simulated data in noisy and reverberant conditions. The results show the superiority of the proposed method in comparison with other previous works.

Impacts of typical atmospheric dispersion schemes on source inversion

Abstract Accurate source inversion for sudden air pollution accidents (SAPAs) relies on forward dispersion models, and the dispersion parameter scheme is the key factor for modeling performance. However, the impacts of different schemes on source inversion have yet to be investigated. In this study, the performance of source inversion based on four empirical power-law dispersion schemes—BRIGGS, SMITH, Pasquill-Gifford (P-G), and Chinese National Standard (CNS) schemes—was evaluated using the data set from the 1956 Prairie Grass experiment. The inversion performance of each dispersion scheme was assessed with both single (source strength) and multiple (source strength and location) unknown source parameters under six different atmospheric stability classes (A, B, C, D, E, and F) and the schemes exhibited substantially different performances. With a single unknown source parameter, the four schemes demonstrated excellent robustness. The estimation results of BRIGGS, P-G, and CNS exhibited comparable accuracy to similar average absolute relative deviations (ARD, 33.3–37.5%) and better accuracy than SMITH. The CNS scheme performed the best (37.6%) in unstable atmospheric conditions (A, B, and C), P-G and BRIGGS were comparable and performed the best (26.3% and 25.4%, respectively) in neutral atmospheric conditions (D), and the four schemes exhibited similar performances (28.3–31.3%) in stable atmospheric conditions (E and F). When location was also unknown, the source strength accuracy ranking was P-G (31.6%) > SMITH (43.2%) > CNS (50.6%) > BRIGGS (80.1%); however, the robustness of the P-G, SMITH, and CNS schemes was poor. Further analysis revealed that the differences in the schemes' inversion errors were significantly higher than those of algorithms from previous studies, which implies that scheme selection had a greater influence on source inversion than the algorithm selection. The results of this study can improve our understanding of the factors that influence source inversion.

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

SummaryThis article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.

Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis

Voice signals acquired by a microphone array often include considerable noise and mutual interference, seriously degrading the accuracy and speed of speech separation. Traditional beamforming is simple to implement, but its source interference suppression is not adequate. In contrast, independent component analysis (ICA) can improve separation, but imposes an iterative and time-consuming process to calculate the separation matrix. As a supporting method, principle component analysis (PCA) contributes to reduce the dimension, retrieve fast results, and disregard false sound sources. Considering the sparsity of frequency components in a mixed signal, we propose an adaptive fast speech separation algorithm based on multiple sound source localization as preprocessing to select between beamforming and frequency domain ICA according to different mixing conditions per frequency bin. First, a fast positioning algorithm allows calculating the maximum number of components per frequency bin of a mixed speech signal to prevent the occurrence of false sound sources. Then, PCA reduces the dimension to adaptively adjust the weight of beamforming and ICA for speech separation. Subsequently, the ICA separation matrix is initialized based on the sound source localization to notably reduce the iteration time and mitigate permutation ambiguity. Simulation and experimental results verify the effectiveness and speedup of the proposed algorithm.

Single and multiple odor source localization using hybrid nature-inspired algorithm

In this paper, optimization-based approach has been adopted to localize the odor source in an unknown environment. Two scenarios taken into consideration, first single odor source (SOS) with a point source emission at a constant rate and four multiple odor sources (MOS) with point source emissions and different release rates constant in time. In context to SOS, four environments that have distinct dimensional layout have been generated with slight variation in wind velocity and diffusion constant. In case of MOS, there are five environments with same layout but different contributing factors such as wind velocity, placement of odor sources and emission rates which are considered to demonstrate its impact on success rate of algorithms. A recent optimization technique called hybrid teaching learning particle swarm optimization (HTLPSO) has been adopted and implemented in all the arenas, namely SOS and MOS, where mobile robots AKA virtual agents (VAs) are working in collaboration. There are group of VAs deployed in this operation ranging from {3–15}. To investigate the effectiveness of the algorithm, results of HTLPSO are compared with classical particle swarm optimization (PSO) and teaching learning-based optimization (TLBO). It is observed that HTLPSO outperforms TLBO and PSO in arenas with larger dimensions while utilizing few iterations in comparison with other algorithms in case of SOS. HTLPSO also performs best in case of MOS, surviving the effect of wind velocity and change in emission rates. Only when odor sources are placed differently and scattered, TLBO gives the best result. Another highlight of HTLPSO is convergence with high accuracy even with less number of VAs.

Multiple dipole source localization of EEG measurements using particle filter with partial stratified resampling.

Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracylimits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.

Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy.

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

Localization of Near-Field Sources for Exact Source-Sensor Spatial Geometry

We propose a cumulant-based algorithm for multiple near-field source localization based on exact source-sensor spatial geometry, where no simplification, such as Fresnel approximation of spatial phase-delay and inconsideration of propagation attenuation is made. We define five cumulant matrices, by which a set of coarse angle-range estimates plus a set of ambiguous angle-range estimates are derived. Fine and unambiguous estimation is finally obtained by a simple refinement step. We define the algorithm's name as Cumulant Algorithm For Exact Model (CAFEM). Compared with previous algorithms, CAFEM has the following advantages: (1) it is simple, analytical, and search-free, (2) it is not necessary to use uniformly spaced linear arrays, and (3) it is applicable to array sensors with unknown amplitude uncertainties and can accommodate any arbitrarily unknown propagation loss.

Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty

Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this method is highly dependent on the propagation model which is not easy to be captured in practice. Moreover, most existing works only consider the single source and the identical measurement noise scenario, while in practice multiple co-channel sources may transmit simultaneously, and the measurement noise tends to be nonuniform. In this paper, we study the multiple co-channel sources localization (MSL) problem under unknown nonuniform noise, while jointly estimating the parametric propagation model. Specifically, we model the MSL problem as being parameterized by the unknown source locations and propagation parameters, and then reformulate it as a joint parametric sparsifying dictionary learning (PSDL) and sparse signal recovery (SSR) problem which is solved under the framework of sparse Bayesian learning with iterative parametric dictionary approximation. Furthermore, multiple snapshot measurements are utilized to improve the localization accuracy, and the Cramer-Rao lower bound (CRLB) is derived to analyze the theoretical estimation error bound. Comparing with the state-of-the-art sparsity-based MSL algorithms as well as CRLB, extensive simulations show the importance of jointly inferring the propagation parameters,and highlight the effectiveness and superiority of the proposed method.

Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines.

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.