A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a hi...Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a highly adaptive decomposition with incoherent ground scattering models(ADIGSM).In ADIGSM,Neumann’s adaptive model is employed to describe volume scattering,and to explain cross-polarized power in remainder matrix,so that we can obtain orientation angle randomness for both volume scattering and the dominant ground scattering.The computation of volume scattering parameters is strictly constrained for non-negative eigenvalues,while the volume scattering parameters that explain the most cross-polarized power are selected.When applying ADIGSM to NASA’s UAVSAR data,the negative component powers were obtained in quite a few forest pixels.Compared with several newest decompositions,the volume scattering power is obviously lowered,especially in areas dominated by surface scattering or double bounce scattering.The orientation angle randomness of each component is reasonable as well.ADIGSM has potential to be applied in the fields such as PolSAR image classification,land cover mapping,speckle filtering,soil moisture and roughness estimation,etc.展开更多
The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix...The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.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
A low-complexity method for direction of arrival（DOA） estimation based on estimation signal parameters via rotational invariance technique（ESPRIT） is proposed.Instead of using the cross-correlation vectors in mult...A low-complexity method for direction of arrival（DOA） estimation based on estimation signal parameters via rotational invariance technique（ESPRIT） is proposed.Instead of using the cross-correlation vectors in multistage Wiener filter（MSWF）,the orthogonal residual vectors obtained in conjugate gradient（CG） method span the signal subspace used by ESPRIT.The computational complexity of the proposed method is significantly reduced,since the signal subspace estimation mainly needs two matrixvector complex multiplications at the iteration of data level.Furthermore,the prior training data are not needed in the proposed method.To overcome performance degradation at low signal to noise ratio（SNR）,the expanded signal subspace spanned by more basis vectors is used and simultaneously renders ESPRIT yield redundant DOAs,which can be excluded by performing ESPRIT once more using the unexpanded signal subspace.Compared with the traditional ESPRIT methods by MSWF and eigenvalue decomposition（EVD）,numerical results demonstrate the satisfactory performance of the proposed method.展开更多
The novel information criterion （NIC） algorithm can find the principal subspace quickly, but it is not an actual principal component analysis （PCA） algorithm and hence it cannot find the orthonormal eigen-space wh...The novel information criterion （NIC） algorithm can find the principal subspace quickly, but it is not an actual principal component analysis （PCA） algorithm and hence it cannot find the orthonormal eigen-space which corresponds to the principal component of input vector. This defect limits its application in practice. By weighting the neural network＇s output of NIC, a modified novel information criterion （MNIC） algorithm is presented. MNIC extractes the principal components and corresponding eigenvectors in a parallel online learning program, and overcomes the NIC＇s defect. It is proved to have a single global optimum and nonquadratic convergence rate, which is superior to the conventional PCA online algorithms such as Oja and LMSER. The relationship among Oja, LMSER and MNIC is exhibited. Simulations show that MNIC could converge to the optimum fast. The validity of MNIC is proved.展开更多
In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. Fi...In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.展开更多
Models based on a parabolic equation(PE)can accurately predict sound propagation problems in range-dependent ocean waveguides.Consequently,this method has developed rapidly in recent years.Compared with normal mode th...Models based on a parabolic equation(PE)can accurately predict sound propagation problems in range-dependent ocean waveguides.Consequently,this method has developed rapidly in recent years.Compared with normal mode theory,PE focuses on numerical calculation,which is difficult to use in the mode domain analysis of sound propagation,such as the calculation of mode phase velocity and group velocity.To broaden the capability of PE models in analyzing the underwater sound field,a wave mode calculation method based on PE is proposed in this study.Step-split Pade PE recursive matrix equations are combined to obtain a propagation matrix.Then,the eigenvalue decomposition technique is applied to the matrix to extract sound mode eigenvalues and eigenfunctions.Numerical experiments on some typical waveguides are performed to test the accuracy and flexibility of the new method.Discussions on different orders of Padéapproximant demonstrate angle limitations in PE and the missing root problem is also discussed to prove the advantage of the new method.The PE mode method can be expanded in the future to solve smooth wave modes in ocean waveguides,including fluctuating boundaries and sound speed profiles.展开更多
Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this pap...Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.展开更多
The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equiv...The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equivalently transformed to a well-known unconstrained problem of matrix equation whose coefficient matrices contain the corresponding eigenvector, and hence the constrained problem can be solved in terms of the eigenvectors of P. A simple and eigenvector-free formula of the general solutions to the constrained problem by generalized inverses of the coefficient matrices A and B is presented. Moreover, a similar problem of the matrix equation with generalized constraint is discussed.展开更多
The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation wh...The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation when the number of antennas at base stations （NABT） tends to infinity, is proposed in this paper. The algorithm is based on the close relationship between covariance matrix of received pilot signal and the channel fast fading coefficient matrix, i.e. the latter is the eigenvector matrix of the former when NABT tends to infinity. Therefore, we can get a set of normalized base vectors from the eigenvalue decomposition （EVD） of sample covariance matrix in practical Massive MIMO networks. By multiplying the received pilot signal with conjugate transpose of normalized base vector matrix, the channel matrix is projected to a lower dimensional matrix, and the intra-cell and inter-cell interference can be eliminated completely when NABT tends to infinity. Thus, we only need to estimate the lower dimensional projected matrix during the channel estimation. Simulation results show that the mean square error （MSE） performance of channel estimation is improved with approximately two orders of magnitude when the signal-to-noise ratio （SNR） is 40 dB, compared with EVD based channel estimation algorithm. And the signal-to-interference ratio （SIR） is improved greatly as well. The increment of SIR becomes larger and larger as SNR increasing.展开更多
In this paper,we combine the generalized multiscale finite element method(GMsFEM)with the balanced truncation(BT)method to address a parameterdependent elliptic problem.Basically,in progress of a model reduction we tr...In this paper,we combine the generalized multiscale finite element method(GMsFEM)with the balanced truncation(BT)method to address a parameterdependent elliptic problem.Basically,in progress of a model reduction we try to obtain accurate solutions with less computational resources.It is realized via a spectral decomposition from the dominant eigenvalues,that is used for an enrichment of multiscale basis functions in the GMsFEM.The multiscale bases computations are localized to specified coarse neighborhoods,and follow an offline-online process in which eigenvalue problems are used to capture the underlying system behaviors.In the BT on reduced scales,we present a local-global strategy where it requires the observability and controllability of solutions to a set of Lyapunov equations.As the Lyapunov equations need expensive computations,the efficiency of our combined approach is shown to be readily flexible with respect to the online space and an reduced dimension.Numerical experiments are provided to validate the robustness of our approach for the parameter-dependent elliptic model.展开更多
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
基金This work was supported by the National Key Basic Research Program of China(973 Program)under grant number 2012 CB719906The authors would like to thank UAVSAR team in Jet Propulsion Laboratory(JPL),NASA for processing and providing UAVSAR data and the reviewers for reviewing this paper.The authors especially thank Dr Naiara Pinto in JPL for her useful suggestions to this paper.
文摘Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a highly adaptive decomposition with incoherent ground scattering models(ADIGSM).In ADIGSM,Neumann’s adaptive model is employed to describe volume scattering,and to explain cross-polarized power in remainder matrix,so that we can obtain orientation angle randomness for both volume scattering and the dominant ground scattering.The computation of volume scattering parameters is strictly constrained for non-negative eigenvalues,while the volume scattering parameters that explain the most cross-polarized power are selected.When applying ADIGSM to NASA’s UAVSAR data,the negative component powers were obtained in quite a few forest pixels.Compared with several newest decompositions,the volume scattering power is obviously lowered,especially in areas dominated by surface scattering or double bounce scattering.The orientation angle randomness of each component is reasonable as well.ADIGSM has potential to be applied in the fields such as PolSAR image classification,land cover mapping,speckle filtering,soil moisture and roughness estimation,etc.
文摘The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.
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.
文摘A low-complexity method for direction of arrival（DOA） estimation based on estimation signal parameters via rotational invariance technique（ESPRIT） is proposed.Instead of using the cross-correlation vectors in multistage Wiener filter（MSWF）,the orthogonal residual vectors obtained in conjugate gradient（CG） method span the signal subspace used by ESPRIT.The computational complexity of the proposed method is significantly reduced,since the signal subspace estimation mainly needs two matrixvector complex multiplications at the iteration of data level.Furthermore,the prior training data are not needed in the proposed method.To overcome performance degradation at low signal to noise ratio（SNR）,the expanded signal subspace spanned by more basis vectors is used and simultaneously renders ESPRIT yield redundant DOAs,which can be excluded by performing ESPRIT once more using the unexpanded signal subspace.Compared with the traditional ESPRIT methods by MSWF and eigenvalue decomposition（EVD）,numerical results demonstrate the satisfactory performance of the proposed method.
文摘The novel information criterion （NIC） algorithm can find the principal subspace quickly, but it is not an actual principal component analysis （PCA） algorithm and hence it cannot find the orthonormal eigen-space which corresponds to the principal component of input vector. This defect limits its application in practice. By weighting the neural network＇s output of NIC, a modified novel information criterion （MNIC） algorithm is presented. MNIC extractes the principal components and corresponding eigenvectors in a parallel online learning program, and overcomes the NIC＇s defect. It is proved to have a single global optimum and nonquadratic convergence rate, which is superior to the conventional PCA online algorithms such as Oja and LMSER. The relationship among Oja, LMSER and MNIC is exhibited. Simulations show that MNIC could converge to the optimum fast. The validity of MNIC is proved.
基金co-supported by Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(No.20105584004)
文摘In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.
基金Project supported by Young Elite Scientist Sponsorship Program by CAST(Grant No.YESS20200330).
文摘Models based on a parabolic equation(PE)can accurately predict sound propagation problems in range-dependent ocean waveguides.Consequently,this method has developed rapidly in recent years.Compared with normal mode theory,PE focuses on numerical calculation,which is difficult to use in the mode domain analysis of sound propagation,such as the calculation of mode phase velocity and group velocity.To broaden the capability of PE models in analyzing the underwater sound field,a wave mode calculation method based on PE is proposed in this study.Step-split Pade PE recursive matrix equations are combined to obtain a propagation matrix.Then,the eigenvalue decomposition technique is applied to the matrix to extract sound mode eigenvalues and eigenfunctions.Numerical experiments on some typical waveguides are performed to test the accuracy and flexibility of the new method.Discussions on different orders of Padéapproximant demonstrate angle limitations in PE and the missing root problem is also discussed to prove the advantage of the new method.The PE mode method can be expanded in the future to solve smooth wave modes in ocean waveguides,including fluctuating boundaries and sound speed profiles.
基金Project supported by the Fundamental Research Funds for the Central Universities, China (Grant No. 2019XD-A02)the National Natural Science Foundation of China (Grant Nos. U1636106, 61671087, 61170272, and 92046001)+2 种基金Natural Science Foundation of Beijing Municipality, China (Grant No. 4182006)Technological Special Project of Guizhou Province, China (Grant No. 20183001)the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (Grant Nos. 2018BDKFJJ016 and 2018BDKFJJ018)。
文摘Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.
基金The work of the first author was supported by the Young Talent Foundation of Zhejiang Gongshang University
文摘The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equivalently transformed to a well-known unconstrained problem of matrix equation whose coefficient matrices contain the corresponding eigenvector, and hence the constrained problem can be solved in terms of the eigenvectors of P. A simple and eigenvector-free formula of the general solutions to the constrained problem by generalized inverses of the coefficient matrices A and B is presented. Moreover, a similar problem of the matrix equation with generalized constraint is discussed.
基金supported by the National Natural Science Foundation of China (61302083, 61272518)
文摘The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation when the number of antennas at base stations （NABT） tends to infinity, is proposed in this paper. The algorithm is based on the close relationship between covariance matrix of received pilot signal and the channel fast fading coefficient matrix, i.e. the latter is the eigenvector matrix of the former when NABT tends to infinity. Therefore, we can get a set of normalized base vectors from the eigenvalue decomposition （EVD） of sample covariance matrix in practical Massive MIMO networks. By multiplying the received pilot signal with conjugate transpose of normalized base vector matrix, the channel matrix is projected to a lower dimensional matrix, and the intra-cell and inter-cell interference can be eliminated completely when NABT tends to infinity. Thus, we only need to estimate the lower dimensional projected matrix during the channel estimation. Simulation results show that the mean square error （MSE） performance of channel estimation is improved with approximately two orders of magnitude when the signal-to-noise ratio （SNR） is 40 dB, compared with EVD based channel estimation algorithm. And the signal-to-interference ratio （SIR） is improved greatly as well. The increment of SIR becomes larger and larger as SNR increasing.
基金The Research is supported by NSFC(Grant Nos.11771224,11301462),Jiangsu Province Qing Lan Project and Jiangsu Overseas Research Program for University Prominent Teachers to Shan Jiang.We would like to thank Professor Yalchin Efendiev in Texas A&M University for many useful discussions.And we appreciate the referees and editors for their insightful comments and helpful suggestions.
文摘In this paper,we combine the generalized multiscale finite element method(GMsFEM)with the balanced truncation(BT)method to address a parameterdependent elliptic problem.Basically,in progress of a model reduction we try to obtain accurate solutions with less computational resources.It is realized via a spectral decomposition from the dominant eigenvalues,that is used for an enrichment of multiscale basis functions in the GMsFEM.The multiscale bases computations are localized to specified coarse neighborhoods,and follow an offline-online process in which eigenvalue problems are used to capture the underlying system behaviors.In the BT on reduced scales,we present a local-global strategy where it requires the observability and controllability of solutions to a set of Lyapunov equations.As the Lyapunov equations need expensive computations,the efficiency of our combined approach is shown to be readily flexible with respect to the online space and an reduced dimension.Numerical experiments are provided to validate the robustness of our approach for the parameter-dependent elliptic model.
