Source Localization Algorithm Research Articles

Leakage Source Location of Hazardous Chemicals Based on the Improved Gray Wolf Optimization Algorithm

To accurately determine the leakage source location and strength during gas leakage accidents, this study compares the concentration obtained from the diffusion model with that measured by the sensor and proposes an improved gray wolf optimization algorithm for leakage source location. This algorithm introduces two improvement strategies. First, a nonlinear convergence factor is introduced to balance the global and local searches of the algorithm. Second, a reverse learning operation is performed on the three individuals with the worst fitness in the contemporary population. The results showed that the location results based on the improved gray wolf optimization algorithm exhibited high accuracy and stability, could quickly and accurately locate the leakage source, and provided data support for emergency disposal of accidents.

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization

Objective. Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy. Approach. To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine. Main result. The proposed method was validated on two MI public datasets (brain–computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods. Significance. The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

HMSL: Source localization based on higher-order Markov propagation

The widespread use of the Internet and social media has brought us great convenience, but it has also exposed us to a lot of false information and malicious attacks. It is vital to accurately locate the source of the harmful spread to prevent it from spreading further. Most previous studies have assumed that the propagation path is memoryless and always the shortest path. This assumption implies the first-order Markov property of propagation paths. This paper takes into account the higher-order Markov property of propagation paths in the source localization problem. Firstly, the problem of source localization based on observers is formulated. Then, we introduce the higher-order Markov property of propagation paths into the problem and propose a reaction–synchronization–diffusion model to model the propagation process on the higher-order network. On this basis, we build a framework named source localization based on higher-order Markov propagation (HMSL), which is compatible with traditional algorithms for source localization. After that, we conducted experiments on a real dataset and found that the HMSL has significant improvement in the source localization compared to the first-order network. Sensitivity analysis indicates that the degree of improvement is significantly influenced by the probability of infection and the proportion of higher-order nodes. Furthermore, we investigated the reason behind the improvement and found that the first-order network creates paths that do not exist within the raw data. When these fake paths are shorter than actual propagation paths, the length of propagation paths and estimated activation time of observers will be underestimated, thus decreasing the accuracy of source localization. The HMSL framework can solve this problem effectively.

The SESAMEEG package: a probabilistic tool for source localization and uncertainty quantification in M/EEG.

Source localization from M/EEG data is a fundamental step in many analysis pipelines, including those aiming at clinical applications such as the pre-surgical evaluation in epilepsy. Among the many available source localization algorithms, SESAME (SEquential SemiAnalytic Montecarlo Estimator) is a Bayesian method that distinguishes itself for several good reasons: it is highly accurate in localizing focal sources with comparably little sensitivity to input parameters; it allows the quantification of the uncertainty of the reconstructed source(s); it accepts user-defined a priori high- and low-probability search regions in input; it can localize the generators of neural oscillations in the frequency domain. Both a Python and a MATLAB implementation of SESAME are available as open-source packages under the name of SESAMEEG and are well integrated with the main software packages used by the M/EEG community; moreover, the algorithm is part of the commercial software BESA Research (from version 7.0 onwards). While SESAMEEG is arguably simpler to use than other source modeling methods, it has a much richer output that deserves to be described thoroughly. In this article, after a gentle mathematical introduction to the algorithm, we provide a complete description of the available output and show several use cases on experimental M/EEG data.

A 3-D spatial–temporal near-field parameter estimation method with two correlation operations

This paper proposes a narrowband near-field (NF) source localization algorithm based on two correlation operations. By utilizing the correlation of incident signal in the time and spatial domains, the algorithm achieves direction-of-arrival (DOA) with high-precision and low-complexity and range estimation under the virtual single snapshot. To do so, in the processing of NF source received signals, this paper first utilizes the symmetry of the cross-cross array and obtains a covariance matrix containing only azimuth and elevation information through two correlation operations. Since the covariance matrix has a standard two-level Toeplitz matrix form, the estimated parameters can be directly extracted from the noise-free covariance matrix without pairing using the two-level Vandermonde decomposition technique. Finally, the estimated value of the range is obtained using a one-dimensional (1-D) spectral peak search. Computer simulation results verify the superior performance of the proposed algorithm in terms of complexity and estimation accuracy.

Parameter estimation and sensitivities of a Bayesian source localization method in an urban environment

Acoustic signals propagating in urban environments are influenced by rough-surface scattering, multipath reflections, and diffraction. The performance of conventional source localization algorithms often suffers when these effects are present. Bayesian approaches, however, are particularly well suited to incorporating physics-based statistical models for the signal propagation. Previously, we found that the complex Wishart distribution, which describes fully saturated scattered signals across a network of receivers, can be readily employed in a Bayesian framework. In this presentation, a new source localization algorithm based on the Wishart distribution signal model is described and tested on real acoustic data. The experimental data were collected within a mock urban environment as part of a NATO urban acoustics-seismics experiment in Walenstadt, Switzerland. Four acoustic arrays recorded signatures from gunshots and ground vehicles. The present study uses the measured signals to investigate the sensitivity and relative importance of the model parameters, including source and noise amplitudes, frequency band, prior signal sampling, and sensor-sensor correlation behavior. Results are presented in the form of source location probability maps and localization error metrics. Interpretations of the study, including strengths and weaknesses of the new method, are discussed.

Simultaneous Source Localization and Formation via a Distributed Sign Gradient-Free Algorithm

This paper develops a distributed sign gradient-free algorithm for simultaneous source localization and formation of a multi-robot system. A distinguished feature of the algorithm is that it takes into account robots' measure noise as well as ternary communication, which significantly reduces the communication cost of the overall network. Considering the presence of noise, the algorithm is designed to be gradient-free. In addition, an inherent connection is established between the selection of control parameters and the convergence property of the sign gradient-free algorithm, as well as signal strength, noisy intensity, formation radius, and the number of informed robots.

A fast algorithm for diffusion source localization in large-scale complex networks

Abstract The identification of the origin of diffusion processes in complex networks is a subject of significant interest across numerous interdisciplinary fields. One approach to solving this issue involves the placement of a few observer nodes within the network and the estimation of the unknown source through the utilization of information gathered by these observer nodes. However, this approach presents certain drawbacks, particularly with regard to computational complexity. To address this limitation, this study introduces an innovative Hill-Climbing algorithm designed to efficiently identify diffusion sources within large-scale complex networks. Our approach, the Local Search Hill Climbing (LSHC) method, transforms the source localization problem into an optimization task, utilizing strategically deployed observer nodes. Experiments conducted on both random and scale-free network models demonstrate that our method significantly reduces computational time while maintaining high accuracy in pinpointing the diffusion source. This approach offers a substantial improvement over traditional methods and holds considerable promise for practical applications in network science.

A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.

Efficient Multi-Sound Source Localization Algorithm for Transformer Faults Based on Polyphase Filters.

Power transformers play a critical role in power systems, and the early detection of their faults and defects, accounting for over 30%, can be achieved through abnormal sound analysis. Sound source localization based on microphone arrays has proven effective in focusing on the troubleshooting scope, preventing potential severe hazards caused by delays in fault removal, and significantly reducing operational and maintenance difficulties and costs. However, existing microphone array-based sound source localization algorithms face challenges in maintaining both accuracy and simplicity and especially suffer from a sharp decrease in performance when dealing with multiple sound sources. This paper presents a multi-sound source localization algorithm for transformer faults based on polyphase filters, integrating the sum-difference monopulse angle measurement technique into the microphone array. Firstly, the signals received from the transformers are divided into multiple subbands using polyphase filters, allowing for multi-source separation and reducing the sampling rate of each subband. Next, the time-domain signals in subbands subject to noise suppression are processed into sum and difference beams. The resulting beam outputs are transformed into frequency-domain signals using the Fast Fourier Transform (FFT), effectively enhancing the signal-to-noise ratio (SNR) for separate sound sources. Finally, each subband undergoes sum-difference monopulse angle measurement in the frequency domain to achieve the high-precision localization of specific faults. The proposed algorithm has been demonstrated to be effective in achieving higher localization accuracy and reducing computational complexity in the presence of actual amplitude-phase errors in microphone arrays. These advantages can facilitate its practical applications. By enabling early targeting of fault sources when abnormalities occur, this algorithm provides valuable assistance to operation and maintenance personnel, thereby enhancing the maintenance efficiency of transformers.

Sparse Bayesian learning based on spatio-temporal structure-aware for matched field processing.

In the application of the matched field processing (MFP) algorithm for underwater acoustic source localization, the measurements at each time step are conventionally processed independently. This study incorporates the prior information about the continuous spatial changes of the source over time under realistic conditions, a factor anticipated to improve localization performance. In this paper, a sparse Bayesian learning (SBL) algorithm based on the spatio-temporal structure-aware is described. We exploit a structure prior for sparse coefficients to capture the continuous spatial structure between adjacent time steps. Moreover, the sparse coefficient can automatically select the update method, utilizing the statistical information from adjacent neighbors or updating independently. The hidden variables in the hierarchical Bayesian framework are inferred via variational Bayesian inference (VBI). Additionally, we extend the proposed method to the multi-frequency case. This method inherits the advantages of the SBL and further reduces position estimation errors. Compared to other approaches, the construction of an accurate motion model is not required. The efficacy of the proposed algorithm is demonstrated through simulation examples and an analysis of the SWellEx-96 experimental data.

Sound Source Localization Algorithm Based on Levenberg_Marquardt

Sound Source Localization Algorithm Based on Levenberg_Marquardt

A Multiharmonic Sources Localization Algorithm Based on ICA and Posterior Harmonic Admittance

A Multiharmonic Sources Localization Algorithm Based on ICA and Posterior Harmonic Admittance

Error analysis of the compact microphone array for sound source localization

Smart sensing is used to provide specific information by using physical quantities measured by multiple sensors or multi-modal sensor modules. Nowadays, the smart sensor is configured as a compact system using very small transducers implemented by MEMS technology. Accordingly, the multi-modal sensing device is getting miniaturized. A microphone array can be classified as a smart sensing device that estimates the sound field information. However, it is difficult to implement a compact system because the measurable frequency range is limited by the spacing between adjacent microphones. Also, the calibration is difficult due to the irregularly shaped sensor element; therefore, the traceability is not yet established. In this study, sound source localization algorithms are applied for implementing the compact microphone array. The inherent error due to the sensor position tolerance, magnitude difference, and phase mismatch is analyzed. Because the coherent noise increases uncertainty in the actual test environment, the estimation error depending on the critical distance is analyzed. Finally, a compact webcam-based multi-modal sensor is implemented and the localization test result is reviewed.

Fuzzy based self-tuned move lengths for enhanced performance of gas source localization algorithm

The world has witnessed a lot of catastrophes in recent times due to chemical gas leaks. The core problem is untimely or sudden happenings of calamity for which humans are not prepared to take appropriate actions. Hence robotic gas source localization can be considered as an alternative to prevent such catastrophes. This paper presents an improved approach to an existing chemotactic plume tracing algorithm with self-tuned move length/step size. The technique uses the proposed fuzzy inference model to produce the move lengths for the next walk based on the input of gas concentration magnitude in the present state. The move lengths correspond to either the plume finding or plume tracing stage with which a mobile robot surges for the next step. Dynamic plumes under eight different simulated environments are created to evaluate the proposed approach rather than plumes in laminar flow for a more realistic case. Performance analysis of the algorithm is based on success rate with self-tuned move length compared with fixed move length. In addition, there is an analysis of step size parameters that vary concerning a particular environmental condition. Results show that adaptive step size can increase the success rate of the plume tracing algorithm and consequently improve search performance and efficiency.

Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

Bayesian source localization in an urban environment using scattered signal distributions

Acoustic signals propagating in urban environments are influenced by rough-surface scattering, multipath reflections, and diffraction. Conventional source localization algorithms often perform poorly when these effects are present. Bayesian approaches, however, are particularly well suited to incorporating physics-based statistical models for the signal propagation. Previously, we found that the complex Wishart distribution, which describes fully saturated scattered signals across a network of receivers, can be readily employed in a Bayesian framework. This approach is very general, as it includes source triangulation and trilateration as special cases. Feasibility was initially demonstrated using simulations. In the present work, we describe an experimental demonstration of Bayesian source localization using data recorded in an urban-like environment. The experimental data were collected as part of a NATO urban acoustics-seismics experiment in Walenstadt, Switzerland, in May 2023. A network of four acoustic nodes, each with 12 microphones, was deployed and recorded emissions from a variety of sources. Initial results from the data processing and localization are described.

Sound source localization method based time-domain signal feature using deep learning

Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.

Dual input neural networks for positional sound source localization

In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones’ coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.