Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is eq...Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.展开更多
Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highl...Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.展开更多
Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requi...Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.展开更多
Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
A new method in digital hearing aids to adaptively localize the speech source in noise and reverberant environment is proposed. Based on the room reverberant model and the multichannel adaptive eigenvalue decompositi...A new method in digital hearing aids to adaptively localize the speech source in noise and reverberant environment is proposed. Based on the room reverberant model and the multichannel adaptive eigenvalue decomposition （MCAED） algorithm, the proposed method can iteratively estimate impulse response coefficients between the speech source and microphones by the adaptive subgradient projection method. Then, it acquires the time delays of microphone pairs, and calculates the source position by the geometric method. Compared with the traditional normal least mean square （NLMS） algorithm, the adaptive subgradient projection method achieves faster and more accurate convergence in a low signal-to-noise ratio （SNR） environment. Simulations for glasses digital hearing aids with four-component square array demonstrate the robust performance of the proposed method.展开更多
Due to the deficiencies in the conventional multiple-receiver localization syste,.ns based on direction of arrival （DOA） such as system complexity of interferometer or array and ampli- tude/phase unbalance between m...Due to the deficiencies in the conventional multiple-receiver localization syste,.ns based on direction of arrival （DOA） such as system complexity of interferometer or array and ampli- tude/phase unbalance between multiple receiving channels and constraint on antenna configuration, a new radiated source localization method using the changing rate of phase difference （CRPD） measured by a long baseline interferometer （LBI） only is studied. To solve the strictly nonlinear problem, a two-stage closed-form solution is proposed. In the first stage, the DOA and its changing rate are estimated from the CRPD of each observer by the pseudolinear least square （PLS） method, and then in the second stage, the source position and velocity are found by another PLS minimiza- tion. The bias of the algorithm caused by the correlation between the measurement matrix and the noise in the second stage is analyzed. To reduce this bias, an instrumental variable （IV） method is derived. A weighted IV estimator is given in order to reduce the estimation variance. The proposed method does not need any initial guess and the computation is small. The Cramer-Rao lower bound （CRLB） and mean square error （MSE） are also analyzed. Simulation results show that the proposed method can be close to the CRLB with moderate Gaussian measurement noise.展开更多
Due to the significant effect of abnormal arrivals on localization accuracy,a novel acoustic emission(AE)source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper.Fi...Due to the significant effect of abnormal arrivals on localization accuracy,a novel acoustic emission(AE)source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper.Firstly,iterative weight estimation is utilized to obtain accurate equation residuals.Secondly,according to the distribution of equation residuals,clustering detection is used to identify and exclude abnormal arrivals.Thirdly,the AE source coordinate is recalculated with remaining normal arrivals.Experimental results of pencil-lead breaks indicate that the proposed method can achieve a better localization result with and without abnormal arrivals.The results of simulation tests further demonstrate that the proposed method possesses higher localization accuracy and robustness under different anomaly ratios and scales;even with abnormal arrivals as high as 30%,the proposed localization method still holds a correct detection rate of 91.85%.展开更多
A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arr...A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.展开更多
Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transf...Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.展开更多
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 p...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 nearfield 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 onedimensional(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.展开更多
This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is ...This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.展开更多
Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model...Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.展开更多
This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing...This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.展开更多
Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables....Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.展开更多
To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave deco...To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.展开更多
A uniform array of scalar-sensors with intersensor spacings over a large aperture size generally offers enhanced resolution and source localization accuracy,but it may also lead to cyclic ambiguity.By exploiting the p...A uniform array of scalar-sensors with intersensor spacings over a large aperture size generally offers enhanced resolution and source localization accuracy,but it may also lead to cyclic ambiguity.By exploiting the polarization information of impinging waves,an electromagnetic vector-sensor array outperforms the unpolarized scalar-sensor array in resolving this cyclic ambiguity.However,the electromagnetic vector-sensor array usually consists of cocentered orthogonal loops and dipoles（COLD）,which is easily subjected to mutual coupling across these cocentered dipoles/loops.As a result,the source localization performance of the COLD array may substantially degrade rather than being improved.This paper proposes a new source localization method with a non-cocentered orthogonal loop and dipole（NCOLD）array.The NCOLD array contains only one dipole or loop on each array grid,and the intersensor spacings are larger than a half-wavelength.Therefore,unlike the COLD array,these well separated dipoles/loops minimize the mutual coupling effects and extend the spatial aperture as well.With the NCOLD array,the proposed method can effciently exploit the polarization information to offer high localization precision.展开更多
A hierarchical wireless sensor networks(WSN) was proposed to estimate the plume source location.Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding t...A hierarchical wireless sensor networks(WSN) was proposed to estimate the plume source location.Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident.The entire surveillant field is divided into several small sub-regions.In each sub-region,the localization algorithm based on the improved particle filter(IPF) was performed to estimate the location.Some improved methods such as weighted centroid,residual resampling were introduced to the IPF algorithm to increase the localization performance.This distributed estimation method eliminates many drawbacks inherent with the traditional centralized optimization method.Simulation results show that localization algorithm is efficient for estimating the plume source location.展开更多
The Steered Response Power(SRP)method works well for sound source localization in noisy and reverberant environment.However,the large computation complexity limits its practical application.In this paper,a fast SRP se...The Steered Response Power(SRP)method works well for sound source localization in noisy and reverberant environment.However,the large computation complexity limits its practical application.In this paper,a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array.The proposed method inspired by the SRP spatial spectrum includes two steps:first,the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field;then,different fine searching operations are performed according to the sound source being in far field or near field.Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conventional SRP-PHAT algorithm.The results show that,the proposed method has a comparative accuracy with the conventional SRP algorithm,and achieves a reduction of 93.62%in computation complexity compared to the conventional SRP algorithm.展开更多
Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cann...Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments.In order to improve localization performance,a novel SSL algorithm using convolutional residual network(CRN)is proposed in this paper.The spatial features including time difference of arrivals(TDOAs)between microphone pairs and steered response power-phase transform(SRPPHAT)spatial spectrum are extracted in each Gammatone sub-band.The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN.The proposed algorithm employ CRN to fuse the spatial features.Since the CRN introduces the residual structure on the basis of the convolutional network,it reduce the difficulty of training procedure and accelerate the convergence of the model.A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth.Through simulation verification,compared with the methods using traditional deep neural network,the proposed algorithm can achieve a better localization performance in SSL task,and provide better generalization capacity to untrained noise and reverberation.展开更多
Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations i...Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61973181)Tsinghua University Initiative Scientific Research Program(Grant No.2018Z05JZY004).
文摘Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.
基金supported by the National Science and Technology Major Project of China (No. 2017-II-003–0015)。
文摘Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.
基金the research result of the 2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(Project No.:2024JGA396).
文摘Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
基金Supported by the National Natural Science Foundation of China (60872073)~~
文摘A new method in digital hearing aids to adaptively localize the speech source in noise and reverberant environment is proposed. Based on the room reverberant model and the multichannel adaptive eigenvalue decomposition （MCAED） algorithm, the proposed method can iteratively estimate impulse response coefficients between the speech source and microphones by the adaptive subgradient projection method. Then, it acquires the time delays of microphone pairs, and calculates the source position by the geometric method. Compared with the traditional normal least mean square （NLMS） algorithm, the adaptive subgradient projection method achieves faster and more accurate convergence in a low signal-to-noise ratio （SNR） environment. Simulations for glasses digital hearing aids with four-component square array demonstrate the robust performance of the proposed method.
基金co-supported by the Foundation of National Defense Key Laboratory of China (No. 9140C860304)the National High Technology Research and Development Program of China (No. 2011AA7072048)
文摘Due to the deficiencies in the conventional multiple-receiver localization syste,.ns based on direction of arrival （DOA） such as system complexity of interferometer or array and ampli- tude/phase unbalance between multiple receiving channels and constraint on antenna configuration, a new radiated source localization method using the changing rate of phase difference （CRPD） measured by a long baseline interferometer （LBI） only is studied. To solve the strictly nonlinear problem, a two-stage closed-form solution is proposed. In the first stage, the DOA and its changing rate are estimated from the CRPD of each observer by the pseudolinear least square （PLS） method, and then in the second stage, the source position and velocity are found by another PLS minimiza- tion. The bias of the algorithm caused by the correlation between the measurement matrix and the noise in the second stage is analyzed. To reduce this bias, an instrumental variable （IV） method is derived. A weighted IV estimator is given in order to reduce the estimation variance. The proposed method does not need any initial guess and the computation is small. The Cramer-Rao lower bound （CRLB） and mean square error （MSE） are also analyzed. Simulation results show that the proposed method can be close to the CRLB with moderate Gaussian measurement noise.
基金financial support provided by the National Natural Science Foundation of China(Grant No.41772313)Hunan Science and Technology Planning Project(Grant No.2019RS3001).
文摘Due to the significant effect of abnormal arrivals on localization accuracy,a novel acoustic emission(AE)source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper.Firstly,iterative weight estimation is utilized to obtain accurate equation residuals.Secondly,according to the distribution of equation residuals,clustering detection is used to identify and exclude abnormal arrivals.Thirdly,the AE source coordinate is recalculated with remaining normal arrivals.Experimental results of pencil-lead breaks indicate that the proposed method can achieve a better localization result with and without abnormal arrivals.The results of simulation tests further demonstrate that the proposed method possesses higher localization accuracy and robustness under different anomaly ratios and scales;even with abnormal arrivals as high as 30%,the proposed localization method still holds a correct detection rate of 91.85%.
基金National High-tech Research and Development Program of China (2010AA7010422,2011AA7014061)
文摘A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.
基金This work is supported by the National Nature Science Foundation of China(NSFC)under Grant No.61571106Jiangsu Natural Science Foundation under Grant No.BK20170757the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant No.17KJD510002.
文摘Microphone array-based sound source localization(SSL)is a challenging task in adverse acoustic scenarios.To address this,a novel SSL algorithm based on deep neural network(DNN)using steered response power-phase transform(SRP-PHAT)spatial spectrum as input feature is presented in this paper.Since the SRP-PHAT spatial power spectrum contains spatial location information,it is adopted as the input feature for sound source localization.DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features.SRP-PHAT at each steering position within a frame is arranged into a vector,which is treated as DNN input.A DNN model which can map the SRP-PHAT spatial spectrum to the azimuth of sound source is learned from the training signals.The azimuth of sound source is estimated through trained DNN model from the testing signals.Experiment results demonstrate that the proposed algorithm significantly improves localization performance whether the training and testing condition setup are the same or not,and is more robust to noise and reverberation.
基金supported by the National Natural Science Foundation of China(62022091,61921001).
文摘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 nearfield 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 onedimensional(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.
基金supported by National Natural Science Foundation of China (No. 60675043)Natural Science Foundation of Zhejiang Province of China (No. Y1090426, No. Y1090956)Technical Project of Zhejiang Province of China (No. 2009C33045)
文摘This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.
基金supported by the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space(KF20202109)the National Natural Science Foundation of China(82004259)the Young Talent Training Project of Guangzhou University of Chinese Medicine(QNYC20190110).
文摘Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(6137116961601167)+2 种基金the Jiangsu Natural Science Foundation(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(K201826)the Fundamental Research Funds for the Central Universities(NE2017103)
文摘This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.
基金The National Natural Science Foundation of China under contract No.11704225the Shandong Provincial Natural Science Foundation under contract No.ZR2016AQ23+1 种基金the State Key Laboratory of Acoustics of Chinese Academy of Sciences under contract No.SKLA201704the National Programe on Global Change and Air-Sea Interaction
文摘Environmental uncertainty represents the limiting factor in matched-field localization. Within a Bayesian framework, both the environmental parameters, and the source parameters are considered to be unknown variables. However, including environmental parameters in multiple-source localization greatly increases the complexity and computational demands of the inverse problem. In the paper, the closed-form maximumlikelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. This paper compares two Bayesian-point-estimation methods： the maximum a posteriori（MAP） approach and the marginal posterior probability density（PPD） approach to source localization. The MAP approach determines the sources locations by maximizing the PPD over all source and environmental parameters. The marginal PPD approach integrates the PPD over the unknowns to obtain a sequence of marginal probability distribution over source range or depth.Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that：（1） For sensitive parameters such as source range, water depth and water sound speed, the MAP solution is better than the marginal PPD solution.（2） For the less sensitive parameters, such as,bottom sound speed, bottom density, bottom attenuation and water sound speed, when the SNR is low, the marginal PPD solution can better smooth the noise, which leads to better performance than the MAP solution.Since the source range and depth are sensitive parameters, the research shows that the MAP approach provides a slightly more reliable method to locate multiple sources in an unknown environment.
基金Project supported by the National Natural Science Foundation of China (Grant No.61001160)the Doctoral Foundation of Ministry of Education (Grant No.20093108120018)the Shanghai Leading Academic Discipline Project (Grant No.S30108)
文摘To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.
基金supported by the Scientifc Research Fund of Zhejiang Provincial Education Department(No.Y201225848)the Scientifc and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2013124)
文摘A uniform array of scalar-sensors with intersensor spacings over a large aperture size generally offers enhanced resolution and source localization accuracy,but it may also lead to cyclic ambiguity.By exploiting the polarization information of impinging waves,an electromagnetic vector-sensor array outperforms the unpolarized scalar-sensor array in resolving this cyclic ambiguity.However,the electromagnetic vector-sensor array usually consists of cocentered orthogonal loops and dipoles（COLD）,which is easily subjected to mutual coupling across these cocentered dipoles/loops.As a result,the source localization performance of the COLD array may substantially degrade rather than being improved.This paper proposes a new source localization method with a non-cocentered orthogonal loop and dipole（NCOLD）array.The NCOLD array contains only one dipole or loop on each array grid,and the intersensor spacings are larger than a half-wavelength.Therefore,unlike the COLD array,these well separated dipoles/loops minimize the mutual coupling effects and extend the spatial aperture as well.With the NCOLD array,the proposed method can effciently exploit the polarization information to offer high localization precision.
基金National High Technology Research and Development Program of China(863Program,No.2004AA412050)
文摘A hierarchical wireless sensor networks(WSN) was proposed to estimate the plume source location.Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident.The entire surveillant field is divided into several small sub-regions.In each sub-region,the localization algorithm based on the improved particle filter(IPF) was performed to estimate the location.Some improved methods such as weighted centroid,residual resampling were introduced to the IPF algorithm to increase the localization performance.This distributed estimation method eliminates many drawbacks inherent with the traditional centralized optimization method.Simulation results show that localization algorithm is efficient for estimating the plume source location.
基金Supported by the National Natural Science Foundation of China(No.61201345)the Beijing Key Laboratory of Advanced Information Science and Network Technology(No.XDXX1308)
文摘The Steered Response Power(SRP)method works well for sound source localization in noisy and reverberant environment.However,the large computation complexity limits its practical application.In this paper,a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array.The proposed method inspired by the SRP spatial spectrum includes two steps:first,the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field;then,different fine searching operations are performed according to the sound source being in far field or near field.Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conventional SRP-PHAT algorithm.The results show that,the proposed method has a comparative accuracy with the conventional SRP algorithm,and achieves a reduction of 93.62%in computation complexity compared to the conventional SRP algorithm.
基金supported by Nature Science Research Project of Higher Education Institutions in Jiangsu Province under Grant No.21KJB510018National Nature Science Foundation of China (NSFC)under Grant No.62001215.
文摘Microphone array-based sound source localization(SSL)is widely used in a variety of occasions such as video conferencing,robotic hearing,speech enhancement,speech recognition and so on.The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments.In order to improve localization performance,a novel SSL algorithm using convolutional residual network(CRN)is proposed in this paper.The spatial features including time difference of arrivals(TDOAs)between microphone pairs and steered response power-phase transform(SRPPHAT)spatial spectrum are extracted in each Gammatone sub-band.The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN.The proposed algorithm employ CRN to fuse the spatial features.Since the CRN introduces the residual structure on the basis of the convolutional network,it reduce the difficulty of training procedure and accelerate the convergence of the model.A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth.Through simulation verification,compared with the methods using traditional deep neural network,the proposed algorithm can achieve a better localization performance in SSL task,and provide better generalization capacity to untrained noise and reverberation.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11374271 and 11374270the Fundamental Research Funds for the Central Universities under Grant No 201513038
文摘Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.
